{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ML_in_Finance-Bias-Variance-Tradeoff\n",
    "# Author: Matthew Dixon\n",
    "# Version: 1.0 (28.4.2020)\n",
    "# License: MIT\n",
    "# Email: matthew.dixon@iit.edu\n",
    "# Notes: tested on Mac OS X running Python 3.6.9 with the following packages:\n",
    "# scikit-learn=0.22.1, numpy=1.18.1, matplotlib=3.1.3, pandas=1.0.3\n",
    "# Citation: Please cite the following reference if this notebook is used for research purposes:\n",
    "# Dixon M.F., Halperin I. and P. Bilokon, Machine Learning in Finance: From Theory to Practice, Springer Graduate Textbook Series, 2020."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Overview\n",
    "The purpose of this notebook is to illustrate the \"bias-variance tradeoff\" or \"bias-variance dilemma\" (see Sections 4 and 5 of Chpt 2), an important concept in statistics which concerns the tradeoff between bias (i.e. in-sample error) and variance (i.e. the change in estimate between training and validation sets) as statistical model complexity is varied.  The bias-variance tradeoff consists in the need to minimise these two sources of error, the variance and bias of an estimator, in order to minimise the mean squared error. Sometimes there is a tradeoff between minimising bias and minimising variance to achieve the least possible MSE. The bias-variance tradeoff explains why complex models tend to over-fit the training data and overly simple models may underfit the training data.\n",
    "\n",
    "In this notebook, various polynomial regression models are compared. Each input, $x$, denotes an angle in radians and the output is a noisy trigonometric function $y=1-\\cos(x) + x + \\epsilon$, where $\\epsilon$ is Gaussian noise. The regression model is of the form $\\hat{y}= \\beta_0 + \\beta_1x + \\dots + \\beta_nx^n$. 200 training samples are generated for training and 80 samples are used for out-of-sample testing.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "from matplotlib import rcParams\n",
    "rcParams['figure.figsize'] = 12, 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define input arrays with angles between 0 and 400 degrees converted to radians"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(10)\n",
    "\n",
    "in_sample_x = np.pi / 180 * np.random.uniform(0, 400, size=200)\n",
    "out_sample_x = np.pi / 180 * np.random.uniform(0, 400, size=80)\n",
    "\n",
    "in_sample_x = np.sort(in_sample_x)\n",
    "out_sample_x = np.sort(out_sample_x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define output arrays containing the corresponding values of $y=1-\\cos(x) + x + \\epsilon$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "in_sample_y = 1 - np.cos(in_sample_x) + in_sample_x + np.random.normal(0, 0.6, len(in_sample_x))\n",
    "out_sample_y = 1 - np.cos(out_sample_x) + out_sample_x + np.random.normal(0, 0.6, len(out_sample_x))\n",
    "\n",
    "data_in = pd.DataFrame(np.column_stack([in_sample_x, in_sample_y]), columns=['x', 'y'])\n",
    "data_out = pd.DataFrame(np.column_stack([out_sample_x, out_sample_y]), columns=['x', 'y'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.subplot(221)\n",
    "plt.plot(data_in['x'], data_in['y'], '.', color='blue', label=\"In sample observations\")\n",
    "plt.plot(data_in['x'], 1 - np.cos(in_sample_x) + in_sample_x, color='red', label=\"True function\")\n",
    "plt.legend(loc = \"lower right\")\n",
    "plt.title('Training sample')\n",
    "plt.xlabel('x (radians)')\n",
    "plt.ylabel('y', rotation=0)\n",
    "plt.subplot(222)\n",
    "plt.plot(data_out['x'], data_out['y'], '.', color='green', label=\"Out of sample observations\")\n",
    "plt.plot(data_out['x'], 1 - np.cos(out_sample_x) + out_sample_x, color='red', label=\"True function\")\n",
    "plt.title('Validation sample')\n",
    "plt.xlabel('x (radians)')\n",
    "plt.ylabel('y', rotation=0)\n",
    "plt.legend(loc = \"lower right\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add a column to the training and validation datasets containing each of the monomials up to the 39th power"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>x_2</th>\n",
       "      <th>x_3</th>\n",
       "      <th>x_4</th>\n",
       "      <th>x_5</th>\n",
       "      <th>x_6</th>\n",
       "      <th>x_7</th>\n",
       "      <th>x_8</th>\n",
       "      <th>x_9</th>\n",
       "      <th>...</th>\n",
       "      <th>x_30</th>\n",
       "      <th>x_31</th>\n",
       "      <th>x_32</th>\n",
       "      <th>x_33</th>\n",
       "      <th>x_34</th>\n",
       "      <th>x_35</th>\n",
       "      <th>x_36</th>\n",
       "      <th>x_37</th>\n",
       "      <th>x_38</th>\n",
       "      <th>x_39</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.064375</td>\n",
       "      <td>-0.941821</td>\n",
       "      <td>0.004144</td>\n",
       "      <td>0.000267</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>7.117394e-08</td>\n",
       "      <td>4.581853e-09</td>\n",
       "      <td>2.949588e-10</td>\n",
       "      <td>1.898810e-11</td>\n",
       "      <td>...</td>\n",
       "      <td>1.826439e-36</td>\n",
       "      <td>1.175778e-37</td>\n",
       "      <td>7.569121e-39</td>\n",
       "      <td>4.872654e-40</td>\n",
       "      <td>3.136792e-41</td>\n",
       "      <td>2.019324e-42</td>\n",
       "      <td>1.299948e-43</td>\n",
       "      <td>8.368474e-45</td>\n",
       "      <td>5.387241e-46</td>\n",
       "      <td>3.468060e-47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.170572</td>\n",
       "      <td>0.591109</td>\n",
       "      <td>0.029095</td>\n",
       "      <td>0.004963</td>\n",
       "      <td>0.000847</td>\n",
       "      <td>0.000144</td>\n",
       "      <td>2.462902e-05</td>\n",
       "      <td>4.201022e-06</td>\n",
       "      <td>7.165768e-07</td>\n",
       "      <td>1.222280e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>9.062235e-24</td>\n",
       "      <td>1.545764e-24</td>\n",
       "      <td>2.636641e-25</td>\n",
       "      <td>4.497372e-26</td>\n",
       "      <td>7.671260e-27</td>\n",
       "      <td>1.308502e-27</td>\n",
       "      <td>2.231939e-28</td>\n",
       "      <td>3.807064e-29</td>\n",
       "      <td>6.493787e-30</td>\n",
       "      <td>1.107659e-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.176376</td>\n",
       "      <td>0.228326</td>\n",
       "      <td>0.031108</td>\n",
       "      <td>0.005487</td>\n",
       "      <td>0.000968</td>\n",
       "      <td>0.000171</td>\n",
       "      <td>3.010448e-05</td>\n",
       "      <td>5.309697e-06</td>\n",
       "      <td>9.365010e-07</td>\n",
       "      <td>1.651759e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>2.472612e-23</td>\n",
       "      <td>4.361085e-24</td>\n",
       "      <td>7.691890e-25</td>\n",
       "      <td>1.356662e-25</td>\n",
       "      <td>2.392821e-26</td>\n",
       "      <td>4.220352e-27</td>\n",
       "      <td>7.443672e-28</td>\n",
       "      <td>1.312882e-28</td>\n",
       "      <td>2.315604e-29</td>\n",
       "      <td>4.084161e-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.326301</td>\n",
       "      <td>0.365242</td>\n",
       "      <td>0.106473</td>\n",
       "      <td>0.034742</td>\n",
       "      <td>0.011336</td>\n",
       "      <td>0.003699</td>\n",
       "      <td>1.207017e-03</td>\n",
       "      <td>3.938512e-04</td>\n",
       "      <td>1.285142e-04</td>\n",
       "      <td>4.193436e-05</td>\n",
       "      <td>...</td>\n",
       "      <td>2.561926e-15</td>\n",
       "      <td>8.359599e-16</td>\n",
       "      <td>2.727749e-16</td>\n",
       "      <td>8.900681e-17</td>\n",
       "      <td>2.904304e-17</td>\n",
       "      <td>9.476785e-18</td>\n",
       "      <td>3.092288e-18</td>\n",
       "      <td>1.009018e-18</td>\n",
       "      <td>3.292438e-19</td>\n",
       "      <td>1.074327e-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.343447</td>\n",
       "      <td>0.069856</td>\n",
       "      <td>0.117956</td>\n",
       "      <td>0.040512</td>\n",
       "      <td>0.013914</td>\n",
       "      <td>0.004779</td>\n",
       "      <td>1.641188e-03</td>\n",
       "      <td>5.636610e-04</td>\n",
       "      <td>1.935877e-04</td>\n",
       "      <td>6.648711e-05</td>\n",
       "      <td>...</td>\n",
       "      <td>1.190670e-14</td>\n",
       "      <td>4.089321e-15</td>\n",
       "      <td>1.404465e-15</td>\n",
       "      <td>4.823593e-16</td>\n",
       "      <td>1.656648e-16</td>\n",
       "      <td>5.689709e-17</td>\n",
       "      <td>1.954114e-17</td>\n",
       "      <td>6.711344e-18</td>\n",
       "      <td>2.304991e-18</td>\n",
       "      <td>7.916422e-19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          x         y       x_2       x_3       x_4       x_5           x_6  \\\n",
       "0  0.064375 -0.941821  0.004144  0.000267  0.000017  0.000001  7.117394e-08   \n",
       "1  0.170572  0.591109  0.029095  0.004963  0.000847  0.000144  2.462902e-05   \n",
       "2  0.176376  0.228326  0.031108  0.005487  0.000968  0.000171  3.010448e-05   \n",
       "3  0.326301  0.365242  0.106473  0.034742  0.011336  0.003699  1.207017e-03   \n",
       "4  0.343447  0.069856  0.117956  0.040512  0.013914  0.004779  1.641188e-03   \n",
       "\n",
       "            x_7           x_8           x_9  ...          x_30          x_31  \\\n",
       "0  4.581853e-09  2.949588e-10  1.898810e-11  ...  1.826439e-36  1.175778e-37   \n",
       "1  4.201022e-06  7.165768e-07  1.222280e-07  ...  9.062235e-24  1.545764e-24   \n",
       "2  5.309697e-06  9.365010e-07  1.651759e-07  ...  2.472612e-23  4.361085e-24   \n",
       "3  3.938512e-04  1.285142e-04  4.193436e-05  ...  2.561926e-15  8.359599e-16   \n",
       "4  5.636610e-04  1.935877e-04  6.648711e-05  ...  1.190670e-14  4.089321e-15   \n",
       "\n",
       "           x_32          x_33          x_34          x_35          x_36  \\\n",
       "0  7.569121e-39  4.872654e-40  3.136792e-41  2.019324e-42  1.299948e-43   \n",
       "1  2.636641e-25  4.497372e-26  7.671260e-27  1.308502e-27  2.231939e-28   \n",
       "2  7.691890e-25  1.356662e-25  2.392821e-26  4.220352e-27  7.443672e-28   \n",
       "3  2.727749e-16  8.900681e-17  2.904304e-17  9.476785e-18  3.092288e-18   \n",
       "4  1.404465e-15  4.823593e-16  1.656648e-16  5.689709e-17  1.954114e-17   \n",
       "\n",
       "           x_37          x_38          x_39  \n",
       "0  8.368474e-45  5.387241e-46  3.468060e-47  \n",
       "1  3.807064e-29  6.493787e-30  1.107659e-30  \n",
       "2  1.312882e-28  2.315604e-29  4.084161e-30  \n",
       "3  1.009018e-18  3.292438e-19  1.074327e-19  \n",
       "4  6.711344e-18  2.304991e-18  7.916422e-19  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(2,40): # (Power of 1 is already there)\n",
    "    colname = 'x_%d' % i # New var will be x_power\n",
    "    data_in[colname] = data_in['x']**i\n",
    "    data_out[colname] = data_out['x']**i\n",
    "\n",
    "data_out.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model fitting and evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function below takes the training and validation sets (as `data1` and `data2` respectively), and using the powers of the inputs $x$ calculated above, fits a polynomial of degree `power` to the training data. \n",
    "\n",
    "If `power` is a key of the `models_to_plot` dictionary, the model of that degree is plotted against the training and testing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Fit the models with increasing polynomial order (i.e. complexity)\n",
    "from sklearn.linear_model import LinearRegression\n",
    "def linear_regression(data1, data2, power, models_to_plot):\n",
    "    # Initialize predictors:\n",
    "    predictors=['x']\n",
    "    if power >= 2:\n",
    "        predictors.extend(['x_%d' % i for i in range(2, power + 1)])\n",
    "    \n",
    "    # Fit the model \n",
    "    linreg = LinearRegression(normalize=True)\n",
    "    linreg.fit(data1[predictors], data1['y'])\n",
    "    y_pred = linreg.predict(data1[predictors])\n",
    "    y_pred_out = linreg.predict(data2[predictors])\n",
    "    \n",
    "    # Check if a plot is to be made for the entered power\n",
    "    if power in models_to_plot:\n",
    "        # If so, it is plotted against the training data \n",
    "        # on the subplot specified by models_to_plot\n",
    "        plt.subplot(models_to_plot[power])\n",
    "        plt.tight_layout()\n",
    "        plt.plot(data1['x'], y_pred, color = 'red')\n",
    "        plt.plot(data1['x'], data1['y'],'.', color ='blue' )\n",
    "        plt.title('Training sample: Plot for power: %d'%power)\n",
    "        # The testing data is plotted on the adjacent subplot\n",
    "        plt.subplot(models_to_plot[power] + 1)\n",
    "        plt.tight_layout()\n",
    "        plt.plot(data1['x'], y_pred, color = 'red')\n",
    "        plt.plot(data2['x'], data2['y'], '.', color = 'green')\n",
    "        plt.title('Validation sample: Plot for power: %d' % power)\n",
    "    \n",
    "    # Return the result in pre-defined format\n",
    "    rss = sum((y_pred-data1['y'])**2) / len(y_pred)\n",
    "    cvrss = sum((y_pred_out-data2['y'])**2) / len(y_pred_out)\n",
    "    ret = [rss, cvrss]\n",
    "    ret.extend([linreg.intercept_])\n",
    "    ret.extend(linreg.coef_)\n",
    "    return ret"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize a dataframe to store the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "col = ['rss', 'cross-rss','intercept'] + ['coef_x_%d' % i for i in range(1,40)]\n",
    "ind = ['model_pow_%d' % i for i in range(1,40)]\n",
    "coef_matrix_simple = pd.DataFrame(index=ind, columns=col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the coefficients of the resulting polynomial for degrees of up to 39, and plot those specified in `models_to_plot`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rss</th>\n",
       "      <th>cross-rss</th>\n",
       "      <th>intercept</th>\n",
       "      <th>coef_x_1</th>\n",
       "      <th>coef_x_2</th>\n",
       "      <th>coef_x_3</th>\n",
       "      <th>coef_x_4</th>\n",
       "      <th>coef_x_5</th>\n",
       "      <th>coef_x_6</th>\n",
       "      <th>coef_x_7</th>\n",
       "      <th>...</th>\n",
       "      <th>coef_x_30</th>\n",
       "      <th>coef_x_31</th>\n",
       "      <th>coef_x_32</th>\n",
       "      <th>coef_x_33</th>\n",
       "      <th>coef_x_34</th>\n",
       "      <th>coef_x_35</th>\n",
       "      <th>coef_x_36</th>\n",
       "      <th>coef_x_37</th>\n",
       "      <th>coef_x_38</th>\n",
       "      <th>coef_x_39</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model_pow_1</th>\n",
       "      <td>0.912809</td>\n",
       "      <td>0.874258</td>\n",
       "      <td>1.05527</td>\n",
       "      <td>0.983744</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_pow_2</th>\n",
       "      <td>0.411945</td>\n",
       "      <td>0.331157</td>\n",
       "      <td>-0.412076</td>\n",
       "      <td>2.32925</td>\n",
       "      <td>-0.204064</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_pow_3</th>\n",
       "      <td>0.365461</td>\n",
       "      <td>0.303618</td>\n",
       "      <td>-0.942916</td>\n",
       "      <td>3.31738</td>\n",
       "      <td>-0.571838</td>\n",
       "      <td>0.0363247</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_pow_4</th>\n",
       "      <td>0.312899</td>\n",
       "      <td>0.285136</td>\n",
       "      <td>-0.280443</td>\n",
       "      <td>1.34423</td>\n",
       "      <td>0.720611</td>\n",
       "      <td>-0.257039</td>\n",
       "      <td>0.0214452</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_pow_5</th>\n",
       "      <td>0.312751</td>\n",
       "      <td>0.283105</td>\n",
       "      <td>-0.23793</td>\n",
       "      <td>1.16249</td>\n",
       "      <td>0.903243</td>\n",
       "      <td>-0.32715</td>\n",
       "      <td>0.0328225</td>\n",
       "      <td>-0.000657337</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_pow_6</th>\n",
       "      <td>0.306086</td>\n",
       "      <td>0.288021</td>\n",
       "      <td>-0.55694</td>\n",
       "      <td>3.01362</td>\n",
       "      <td>-1.77005</td>\n",
       "      <td>1.22738</td>\n",
       "      <td>-0.391725</td>\n",
       "      <td>0.0537696</td>\n",
       "      <td>-0.0026415</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_pow_7</th>\n",
       "      <td>0.306056</td>\n",
       "      <td>0.287252</td>\n",
       "      <td>-0.583212</td>\n",
       "      <td>3.20612</td>\n",
       "      <td>-2.13539</td>\n",
       "      <td>1.51632</td>\n",
       "      <td>-0.505468</td>\n",
       "      <td>0.0773017</td>\n",
       "      <td>-0.00508955</td>\n",
       "      <td>0.000100927</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_pow_8</th>\n",
       "      <td>0.299259</td>\n",
       "      <td>0.308579</td>\n",
       "      <td>-1.01443</td>\n",
       "      <td>7.11017</td>\n",
       "      <td>-11.6981</td>\n",
       "      <td>11.5659</td>\n",
       "      <td>-5.95155</td>\n",
       "      <td>1.72003</td>\n",
       "      <td>-0.283418</td>\n",
       "      <td>0.0248609</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_pow_9</th>\n",
       "      <td>0.299091</td>\n",
       "      <td>0.304813</td>\n",
       "      <td>-1.08859</td>\n",
       "      <td>7.92903</td>\n",
       "      <td>-14.2201</td>\n",
       "      <td>14.9492</td>\n",
       "      <td>-8.33942</td>\n",
       "      <td>2.69054</td>\n",
       "      <td>-0.518501</td>\n",
       "      <td>0.0583529</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_pow_10</th>\n",
       "      <td>0.295902</td>\n",
       "      <td>0.313004</td>\n",
       "      <td>-1.46452</td>\n",
       "      <td>12.8315</td>\n",
       "      <td>-32.4166</td>\n",
       "      <td>44.744</td>\n",
       "      <td>-34.4515</td>\n",
       "      <td>16.2043</td>\n",
       "      <td>-4.84808</td>\n",
       "      <td>0.926304</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_pow_11</th>\n",
       "      <td>0.295881</td>\n",
       "      <td>0.314591</td>\n",
       "      <td>-1.4359</td>\n",
       "      <td>12.388</td>\n",
       "      <td>-30.4229</td>\n",
       "      <td>40.74</td>\n",
       "      <td>-30.0915</td>\n",
       "      <td>13.3551</td>\n",
       "      <td>-3.66828</td>\n",
       "      <td>0.609399</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_pow_12</th>\n",
       "      <td>0.295836</td>\n",
       "      <td>0.314637</td>\n",
       "      <td>-1.47943</td>\n",
       "      <td>13.2001</td>\n",
       "      <td>-34.8156</td>\n",
       "      <td>51.3389</td>\n",
       "      <td>-43.987</td>\n",
       "      <td>24.3721</td>\n",
       "      <td>-9.28424</td>\n",
       "      <td>2.51096</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_pow_13</th>\n",
       "      <td>0.295788</td>\n",
       "      <td>0.315431</td>\n",
       "      <td>-1.42819</td>\n",
       "      <td>12.0851</td>\n",
       "      <td>-27.8459</td>\n",
       "      <td>31.8861</td>\n",
       "      <td>-14.2888</td>\n",
       "      <td>-3.34503</td>\n",
       "      <td>7.59355</td>\n",
       "      <td>-4.45094</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_pow_14</th>\n",
       "      <td>0.295614</td>\n",
       "      <td>0.317883</td>\n",
       "      <td>-1.53851</td>\n",
       "      <td>14.8042</td>\n",
       "      <td>-47.0435</td>\n",
       "      <td>92.9316</td>\n",
       "      <td>-121.602</td>\n",
       "      <td>113.275</td>\n",
       "      <td>-76.1186</td>\n",
       "      <td>36.8633</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_pow_15</th>\n",
       "      <td>0.295554</td>\n",
       "      <td>0.319279</td>\n",
       "      <td>-1.47127</td>\n",
       "      <td>12.9512</td>\n",
       "      <td>-32.3579</td>\n",
       "      <td>39.8969</td>\n",
       "      <td>-14.6993</td>\n",
       "      <td>-21.0427</td>\n",
       "      <td>36.3279</td>\n",
       "      <td>-28.5455</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_pow_16</th>\n",
       "      <td>0.291493</td>\n",
       "      <td>0.322724</td>\n",
       "      <td>-0.943563</td>\n",
       "      <td>-3.53815</td>\n",
       "      <td>116.534</td>\n",
       "      <td>-577.863</td>\n",
       "      <td>1421.99</td>\n",
       "      <td>-2110.51</td>\n",
       "      <td>2069.93</td>\n",
       "      <td>-1412.6</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_pow_17</th>\n",
       "      <td>0.286942</td>\n",
       "      <td>0.326335</td>\n",
       "      <td>-1.50996</td>\n",
       "      <td>16.726</td>\n",
       "      <td>-92.2572</td>\n",
       "      <td>410.308</td>\n",
       "      <td>-1195.47</td>\n",
       "      <td>2226.39</td>\n",
       "      <td>-2751.78</td>\n",
       "      <td>2355.06</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_pow_18</th>\n",
       "      <td>0.286903</td>\n",
       "      <td>0.324347</td>\n",
       "      <td>-1.56917</td>\n",
       "      <td>19.1152</td>\n",
       "      <td>-119.793</td>\n",
       "      <td>555.706</td>\n",
       "      <td>-1625.15</td>\n",
       "      <td>3022.8</td>\n",
       "      <td>-3746.73</td>\n",
       "      <td>3233.9</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_pow_19</th>\n",
       "      <td>0.284677</td>\n",
       "      <td>0.342462</td>\n",
       "      <td>-2.0966</td>\n",
       "      <td>42.3426</td>\n",
       "      <td>-410.981</td>\n",
       "      <td>2232</td>\n",
       "      <td>-7051.3</td>\n",
       "      <td>14102.7</td>\n",
       "      <td>-19090.4</td>\n",
       "      <td>18356.3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_pow_20</th>\n",
       "      <td>0.283753</td>\n",
       "      <td>0.348615</td>\n",
       "      <td>-1.71404</td>\n",
       "      <td>24.2257</td>\n",
       "      <td>-164.491</td>\n",
       "      <td>680.274</td>\n",
       "      <td>-1516.18</td>\n",
       "      <td>1565.47</td>\n",
       "      <td>277.423</td>\n",
       "      <td>-3050.42</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_pow_21</th>\n",
       "      <td>0.281549</td>\n",
       "      <td>0.323036</td>\n",
       "      <td>-0.921495</td>\n",
       "      <td>-14.6035</td>\n",
       "      <td>401.206</td>\n",
       "      <td>-3171.86</td>\n",
       "      <td>13449.3</td>\n",
       "      <td>-35539.9</td>\n",
       "      <td>63288.7</td>\n",
       "      <td>-79924.7</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_pow_22</th>\n",
       "      <td>0.281603</td>\n",
       "      <td>0.323002</td>\n",
       "      <td>-1.12157</td>\n",
       "      <td>-5.32436</td>\n",
       "      <td>258.307</td>\n",
       "      <td>-2129.75</td>\n",
       "      <td>9088.27</td>\n",
       "      <td>-23834.1</td>\n",
       "      <td>41662.8</td>\n",
       "      <td>-51076.5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_pow_23</th>\n",
       "      <td>0.281431</td>\n",
       "      <td>0.329748</td>\n",
       "      <td>-0.720567</td>\n",
       "      <td>-25.7536</td>\n",
       "      <td>565.404</td>\n",
       "      <td>-4304.62</td>\n",
       "      <td>17918.9</td>\n",
       "      <td>-46768</td>\n",
       "      <td>82474.9</td>\n",
       "      <td>-103199</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_pow_24</th>\n",
       "      <td>0.281141</td>\n",
       "      <td>0.333487</td>\n",
       "      <td>-0.874132</td>\n",
       "      <td>-17.925</td>\n",
       "      <td>443.141</td>\n",
       "      <td>-3398.46</td>\n",
       "      <td>14053</td>\n",
       "      <td>-36180.5</td>\n",
       "      <td>62531.8</td>\n",
       "      <td>-76129.5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_pow_25</th>\n",
       "      <td>0.280909</td>\n",
       "      <td>0.341251</td>\n",
       "      <td>-0.928941</td>\n",
       "      <td>-15.3692</td>\n",
       "      <td>402.796</td>\n",
       "      <td>-3082.39</td>\n",
       "      <td>12609</td>\n",
       "      <td>-31956</td>\n",
       "      <td>54107.1</td>\n",
       "      <td>-64164</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_pow_26</th>\n",
       "      <td>0.280604</td>\n",
       "      <td>0.360496</td>\n",
       "      <td>-1.26732</td>\n",
       "      <td>2.00912</td>\n",
       "      <td>141.978</td>\n",
       "      <td>-1228.02</td>\n",
       "      <td>5022.86</td>\n",
       "      <td>-12089.8</td>\n",
       "      <td>18517</td>\n",
       "      <td>-18590.7</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_pow_27</th>\n",
       "      <td>0.280665</td>\n",
       "      <td>0.364188</td>\n",
       "      <td>-1.22234</td>\n",
       "      <td>0.119314</td>\n",
       "      <td>167.742</td>\n",
       "      <td>-1411.29</td>\n",
       "      <td>5821.71</td>\n",
       "      <td>-14401.8</td>\n",
       "      <td>23190.6</td>\n",
       "      <td>-25424.9</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_pow_28</th>\n",
       "      <td>0.280656</td>\n",
       "      <td>0.36065</td>\n",
       "      <td>-1.15172</td>\n",
       "      <td>-3.63584</td>\n",
       "      <td>225.124</td>\n",
       "      <td>-1820.93</td>\n",
       "      <td>7490.71</td>\n",
       "      <td>-18732.8</td>\n",
       "      <td>30850.2</td>\n",
       "      <td>-35075.8</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_pow_29</th>\n",
       "      <td>0.280657</td>\n",
       "      <td>0.360751</td>\n",
       "      <td>-1.16493</td>\n",
       "      <td>-2.95703</td>\n",
       "      <td>214.867</td>\n",
       "      <td>-1748.19</td>\n",
       "      <td>7195.85</td>\n",
       "      <td>-17971.4</td>\n",
       "      <td>29511.5</td>\n",
       "      <td>-33401.6</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_pow_30</th>\n",
       "      <td>0.280647</td>\n",
       "      <td>0.363892</td>\n",
       "      <td>-1.11968</td>\n",
       "      <td>-5.3769</td>\n",
       "      <td>252.495</td>\n",
       "      <td>-2023.54</td>\n",
       "      <td>8349.86</td>\n",
       "      <td>-21055</td>\n",
       "      <td>35124.8</td>\n",
       "      <td>-40670.5</td>\n",
       "      <td>...</td>\n",
       "      <td>4.85722e-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_pow_31</th>\n",
       "      <td>0.280571</td>\n",
       "      <td>0.371617</td>\n",
       "      <td>-1.0703</td>\n",
       "      <td>-7.96737</td>\n",
       "      <td>292.709</td>\n",
       "      <td>-2312.69</td>\n",
       "      <td>9524.14</td>\n",
       "      <td>-24058</td>\n",
       "      <td>40289.7</td>\n",
       "      <td>-46895.3</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.13082e-13</td>\n",
       "      <td>2.23892e-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_pow_32</th>\n",
       "      <td>0.280404</td>\n",
       "      <td>0.384969</td>\n",
       "      <td>-0.986925</td>\n",
       "      <td>-12.2574</td>\n",
       "      <td>355.39</td>\n",
       "      <td>-2737.64</td>\n",
       "      <td>11152.1</td>\n",
       "      <td>-27978.1</td>\n",
       "      <td>46611.5</td>\n",
       "      <td>-53988.4</td>\n",
       "      <td>...</td>\n",
       "      <td>4.46637e-13</td>\n",
       "      <td>-2.44345e-14</td>\n",
       "      <td>5.16394e-16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_pow_33</th>\n",
       "      <td>0.280154</td>\n",
       "      <td>0.40222</td>\n",
       "      <td>-0.925889</td>\n",
       "      <td>-15.0653</td>\n",
       "      <td>393.75</td>\n",
       "      <td>-2978.33</td>\n",
       "      <td>11989</td>\n",
       "      <td>-29753.6</td>\n",
       "      <td>49012.9</td>\n",
       "      <td>-56042.2</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.01383e-13</td>\n",
       "      <td>6.50257e-14</td>\n",
       "      <td>-3.92095e-15</td>\n",
       "      <td>8.83102e-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_pow_34</th>\n",
       "      <td>0.278293</td>\n",
       "      <td>0.531338</td>\n",
       "      <td>-1.86681</td>\n",
       "      <td>34.7474</td>\n",
       "      <td>-382.253</td>\n",
       "      <td>2711.3</td>\n",
       "      <td>-11869</td>\n",
       "      <td>33900.5</td>\n",
       "      <td>-66362.8</td>\n",
       "      <td>92148.3</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.11417e-13</td>\n",
       "      <td>-5.57381e-13</td>\n",
       "      <td>8.06509e-14</td>\n",
       "      <td>-4.61227e-15</td>\n",
       "      <td>1.00061e-16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_pow_35</th>\n",
       "      <td>0.280985</td>\n",
       "      <td>0.773645</td>\n",
       "      <td>-0.970811</td>\n",
       "      <td>-9.85716</td>\n",
       "      <td>263.106</td>\n",
       "      <td>-1636.16</td>\n",
       "      <td>4786.89</td>\n",
       "      <td>-6649.69</td>\n",
       "      <td>839.955</td>\n",
       "      <td>12837.3</td>\n",
       "      <td>...</td>\n",
       "      <td>2.3089e-12</td>\n",
       "      <td>-2.0034e-13</td>\n",
       "      <td>-5.40856e-14</td>\n",
       "      <td>1.00677e-14</td>\n",
       "      <td>-6.43765e-16</td>\n",
       "      <td>1.50451e-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_pow_36</th>\n",
       "      <td>0.28036</td>\n",
       "      <td>0.790299</td>\n",
       "      <td>-0.984483</td>\n",
       "      <td>-9.33938</td>\n",
       "      <td>259.848</td>\n",
       "      <td>-1666.82</td>\n",
       "      <td>5205.91</td>\n",
       "      <td>-8668.12</td>\n",
       "      <td>6316.31</td>\n",
       "      <td>3259.57</td>\n",
       "      <td>...</td>\n",
       "      <td>1.26569e-12</td>\n",
       "      <td>1.51413e-13</td>\n",
       "      <td>-2.50999e-14</td>\n",
       "      <td>-3.84612e-15</td>\n",
       "      <td>9.2557e-16</td>\n",
       "      <td>-6.50458e-17</td>\n",
       "      <td>1.61389e-18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_pow_37</th>\n",
       "      <td>0.278224</td>\n",
       "      <td>0.537296</td>\n",
       "      <td>-1.17214</td>\n",
       "      <td>-1.94386</td>\n",
       "      <td>187.964</td>\n",
       "      <td>-1466.43</td>\n",
       "      <td>5672.73</td>\n",
       "      <td>-13080.3</td>\n",
       "      <td>19352.9</td>\n",
       "      <td>-19003.5</td>\n",
       "      <td>...</td>\n",
       "      <td>5.57571e-14</td>\n",
       "      <td>4.664e-14</td>\n",
       "      <td>2.30596e-15</td>\n",
       "      <td>-1.01317e-15</td>\n",
       "      <td>-8.10121e-17</td>\n",
       "      <td>3.05457e-17</td>\n",
       "      <td>-2.43645e-18</td>\n",
       "      <td>6.5338e-20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_pow_38</th>\n",
       "      <td>0.287495</td>\n",
       "      <td>0.669778</td>\n",
       "      <td>-0.775011</td>\n",
       "      <td>1.41042</td>\n",
       "      <td>47.9285</td>\n",
       "      <td>-310.304</td>\n",
       "      <td>794.081</td>\n",
       "      <td>-280.459</td>\n",
       "      <td>-3343.57</td>\n",
       "      <td>9465.4</td>\n",
       "      <td>...</td>\n",
       "      <td>3.52614e-13</td>\n",
       "      <td>3.64975e-14</td>\n",
       "      <td>-2.35645e-15</td>\n",
       "      <td>-1.12968e-15</td>\n",
       "      <td>4.01881e-17</td>\n",
       "      <td>2.06867e-17</td>\n",
       "      <td>-2.43159e-18</td>\n",
       "      <td>9.4776e-20</td>\n",
       "      <td>-9.61133e-22</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_pow_39</th>\n",
       "      <td>0.29606</td>\n",
       "      <td>0.753893</td>\n",
       "      <td>-0.654527</td>\n",
       "      <td>4.0153</td>\n",
       "      <td>-20.7125</td>\n",
       "      <td>200.214</td>\n",
       "      <td>-1180.86</td>\n",
       "      <td>4425.5</td>\n",
       "      <td>-10791.4</td>\n",
       "      <td>17600.6</td>\n",
       "      <td>...</td>\n",
       "      <td>1.26746e-13</td>\n",
       "      <td>4.90284e-14</td>\n",
       "      <td>3.4625e-15</td>\n",
       "      <td>-9.32913e-16</td>\n",
       "      <td>-1.11914e-16</td>\n",
       "      <td>1.34592e-17</td>\n",
       "      <td>2.54811e-18</td>\n",
       "      <td>-4.62747e-19</td>\n",
       "      <td>2.59603e-20</td>\n",
       "      <td>-4.9882e-22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>39 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   rss cross-rss intercept  coef_x_1  coef_x_2   coef_x_3  \\\n",
       "model_pow_1   0.912809  0.874258   1.05527  0.983744       NaN        NaN   \n",
       "model_pow_2   0.411945  0.331157 -0.412076   2.32925 -0.204064        NaN   \n",
       "model_pow_3   0.365461  0.303618 -0.942916   3.31738 -0.571838  0.0363247   \n",
       "model_pow_4   0.312899  0.285136 -0.280443   1.34423  0.720611  -0.257039   \n",
       "model_pow_5   0.312751  0.283105  -0.23793   1.16249  0.903243   -0.32715   \n",
       "model_pow_6   0.306086  0.288021  -0.55694   3.01362  -1.77005    1.22738   \n",
       "model_pow_7   0.306056  0.287252 -0.583212   3.20612  -2.13539    1.51632   \n",
       "model_pow_8   0.299259  0.308579  -1.01443   7.11017  -11.6981    11.5659   \n",
       "model_pow_9   0.299091  0.304813  -1.08859   7.92903  -14.2201    14.9492   \n",
       "model_pow_10  0.295902  0.313004  -1.46452   12.8315  -32.4166     44.744   \n",
       "model_pow_11  0.295881  0.314591   -1.4359    12.388  -30.4229      40.74   \n",
       "model_pow_12  0.295836  0.314637  -1.47943   13.2001  -34.8156    51.3389   \n",
       "model_pow_13  0.295788  0.315431  -1.42819   12.0851  -27.8459    31.8861   \n",
       "model_pow_14  0.295614  0.317883  -1.53851   14.8042  -47.0435    92.9316   \n",
       "model_pow_15  0.295554  0.319279  -1.47127   12.9512  -32.3579    39.8969   \n",
       "model_pow_16  0.291493  0.322724 -0.943563  -3.53815   116.534   -577.863   \n",
       "model_pow_17  0.286942  0.326335  -1.50996    16.726  -92.2572    410.308   \n",
       "model_pow_18  0.286903  0.324347  -1.56917   19.1152  -119.793    555.706   \n",
       "model_pow_19  0.284677  0.342462   -2.0966   42.3426  -410.981       2232   \n",
       "model_pow_20  0.283753  0.348615  -1.71404   24.2257  -164.491    680.274   \n",
       "model_pow_21  0.281549  0.323036 -0.921495  -14.6035   401.206   -3171.86   \n",
       "model_pow_22  0.281603  0.323002  -1.12157  -5.32436   258.307   -2129.75   \n",
       "model_pow_23  0.281431  0.329748 -0.720567  -25.7536   565.404   -4304.62   \n",
       "model_pow_24  0.281141  0.333487 -0.874132   -17.925   443.141   -3398.46   \n",
       "model_pow_25  0.280909  0.341251 -0.928941  -15.3692   402.796   -3082.39   \n",
       "model_pow_26  0.280604  0.360496  -1.26732   2.00912   141.978   -1228.02   \n",
       "model_pow_27  0.280665  0.364188  -1.22234  0.119314   167.742   -1411.29   \n",
       "model_pow_28  0.280656   0.36065  -1.15172  -3.63584   225.124   -1820.93   \n",
       "model_pow_29  0.280657  0.360751  -1.16493  -2.95703   214.867   -1748.19   \n",
       "model_pow_30  0.280647  0.363892  -1.11968   -5.3769   252.495   -2023.54   \n",
       "model_pow_31  0.280571  0.371617   -1.0703  -7.96737   292.709   -2312.69   \n",
       "model_pow_32  0.280404  0.384969 -0.986925  -12.2574    355.39   -2737.64   \n",
       "model_pow_33  0.280154   0.40222 -0.925889  -15.0653    393.75   -2978.33   \n",
       "model_pow_34  0.278293  0.531338  -1.86681   34.7474  -382.253     2711.3   \n",
       "model_pow_35  0.280985  0.773645 -0.970811  -9.85716   263.106   -1636.16   \n",
       "model_pow_36   0.28036  0.790299 -0.984483  -9.33938   259.848   -1666.82   \n",
       "model_pow_37  0.278224  0.537296  -1.17214  -1.94386   187.964   -1466.43   \n",
       "model_pow_38  0.287495  0.669778 -0.775011   1.41042   47.9285   -310.304   \n",
       "model_pow_39   0.29606  0.753893 -0.654527    4.0153  -20.7125    200.214   \n",
       "\n",
       "               coef_x_4     coef_x_5    coef_x_6     coef_x_7  ...  \\\n",
       "model_pow_1         NaN          NaN         NaN          NaN  ...   \n",
       "model_pow_2         NaN          NaN         NaN          NaN  ...   \n",
       "model_pow_3         NaN          NaN         NaN          NaN  ...   \n",
       "model_pow_4   0.0214452          NaN         NaN          NaN  ...   \n",
       "model_pow_5   0.0328225 -0.000657337         NaN          NaN  ...   \n",
       "model_pow_6   -0.391725    0.0537696  -0.0026415          NaN  ...   \n",
       "model_pow_7   -0.505468    0.0773017 -0.00508955  0.000100927  ...   \n",
       "model_pow_8    -5.95155      1.72003   -0.283418    0.0248609  ...   \n",
       "model_pow_9    -8.33942      2.69054   -0.518501    0.0583529  ...   \n",
       "model_pow_10   -34.4515      16.2043    -4.84808     0.926304  ...   \n",
       "model_pow_11   -30.0915      13.3551    -3.66828     0.609399  ...   \n",
       "model_pow_12    -43.987      24.3721    -9.28424      2.51096  ...   \n",
       "model_pow_13   -14.2888     -3.34503     7.59355     -4.45094  ...   \n",
       "model_pow_14   -121.602      113.275    -76.1186      36.8633  ...   \n",
       "model_pow_15   -14.6993     -21.0427     36.3279     -28.5455  ...   \n",
       "model_pow_16    1421.99     -2110.51     2069.93      -1412.6  ...   \n",
       "model_pow_17   -1195.47      2226.39    -2751.78      2355.06  ...   \n",
       "model_pow_18   -1625.15       3022.8    -3746.73       3233.9  ...   \n",
       "model_pow_19    -7051.3      14102.7    -19090.4      18356.3  ...   \n",
       "model_pow_20   -1516.18      1565.47     277.423     -3050.42  ...   \n",
       "model_pow_21    13449.3     -35539.9     63288.7     -79924.7  ...   \n",
       "model_pow_22    9088.27     -23834.1     41662.8     -51076.5  ...   \n",
       "model_pow_23    17918.9       -46768     82474.9      -103199  ...   \n",
       "model_pow_24      14053     -36180.5     62531.8     -76129.5  ...   \n",
       "model_pow_25      12609       -31956     54107.1       -64164  ...   \n",
       "model_pow_26    5022.86     -12089.8       18517     -18590.7  ...   \n",
       "model_pow_27    5821.71     -14401.8     23190.6     -25424.9  ...   \n",
       "model_pow_28    7490.71     -18732.8     30850.2     -35075.8  ...   \n",
       "model_pow_29    7195.85     -17971.4     29511.5     -33401.6  ...   \n",
       "model_pow_30    8349.86       -21055     35124.8     -40670.5  ...   \n",
       "model_pow_31    9524.14       -24058     40289.7     -46895.3  ...   \n",
       "model_pow_32    11152.1     -27978.1     46611.5     -53988.4  ...   \n",
       "model_pow_33      11989     -29753.6     49012.9     -56042.2  ...   \n",
       "model_pow_34     -11869      33900.5    -66362.8      92148.3  ...   \n",
       "model_pow_35    4786.89     -6649.69     839.955      12837.3  ...   \n",
       "model_pow_36    5205.91     -8668.12     6316.31      3259.57  ...   \n",
       "model_pow_37    5672.73     -13080.3     19352.9     -19003.5  ...   \n",
       "model_pow_38    794.081     -280.459    -3343.57       9465.4  ...   \n",
       "model_pow_39   -1180.86       4425.5    -10791.4      17600.6  ...   \n",
       "\n",
       "                coef_x_30    coef_x_31    coef_x_32    coef_x_33    coef_x_34  \\\n",
       "model_pow_1           NaN          NaN          NaN          NaN          NaN   \n",
       "model_pow_2           NaN          NaN          NaN          NaN          NaN   \n",
       "model_pow_3           NaN          NaN          NaN          NaN          NaN   \n",
       "model_pow_4           NaN          NaN          NaN          NaN          NaN   \n",
       "model_pow_5           NaN          NaN          NaN          NaN          NaN   \n",
       "model_pow_6           NaN          NaN          NaN          NaN          NaN   \n",
       "model_pow_7           NaN          NaN          NaN          NaN          NaN   \n",
       "model_pow_8           NaN          NaN          NaN          NaN          NaN   \n",
       "model_pow_9           NaN          NaN          NaN          NaN          NaN   \n",
       "model_pow_10          NaN          NaN          NaN          NaN          NaN   \n",
       "model_pow_11          NaN          NaN          NaN          NaN          NaN   \n",
       "model_pow_12          NaN          NaN          NaN          NaN          NaN   \n",
       "model_pow_13          NaN          NaN          NaN          NaN          NaN   \n",
       "model_pow_14          NaN          NaN          NaN          NaN          NaN   \n",
       "model_pow_15          NaN          NaN          NaN          NaN          NaN   \n",
       "model_pow_16          NaN          NaN          NaN          NaN          NaN   \n",
       "model_pow_17          NaN          NaN          NaN          NaN          NaN   \n",
       "model_pow_18          NaN          NaN          NaN          NaN          NaN   \n",
       "model_pow_19          NaN          NaN          NaN          NaN          NaN   \n",
       "model_pow_20          NaN          NaN          NaN          NaN          NaN   \n",
       "model_pow_21          NaN          NaN          NaN          NaN          NaN   \n",
       "model_pow_22          NaN          NaN          NaN          NaN          NaN   \n",
       "model_pow_23          NaN          NaN          NaN          NaN          NaN   \n",
       "model_pow_24          NaN          NaN          NaN          NaN          NaN   \n",
       "model_pow_25          NaN          NaN          NaN          NaN          NaN   \n",
       "model_pow_26          NaN          NaN          NaN          NaN          NaN   \n",
       "model_pow_27          NaN          NaN          NaN          NaN          NaN   \n",
       "model_pow_28          NaN          NaN          NaN          NaN          NaN   \n",
       "model_pow_29          NaN          NaN          NaN          NaN          NaN   \n",
       "model_pow_30  4.85722e-15          NaN          NaN          NaN          NaN   \n",
       "model_pow_31 -1.13082e-13  2.23892e-15          NaN          NaN          NaN   \n",
       "model_pow_32  4.46637e-13 -2.44345e-14  5.16394e-16          NaN          NaN   \n",
       "model_pow_33 -4.01383e-13  6.50257e-14 -3.92095e-15  8.83102e-17          NaN   \n",
       "model_pow_34 -6.11417e-13 -5.57381e-13  8.06509e-14 -4.61227e-15  1.00061e-16   \n",
       "model_pow_35   2.3089e-12  -2.0034e-13 -5.40856e-14  1.00677e-14 -6.43765e-16   \n",
       "model_pow_36  1.26569e-12  1.51413e-13 -2.50999e-14 -3.84612e-15   9.2557e-16   \n",
       "model_pow_37  5.57571e-14    4.664e-14  2.30596e-15 -1.01317e-15 -8.10121e-17   \n",
       "model_pow_38  3.52614e-13  3.64975e-14 -2.35645e-15 -1.12968e-15  4.01881e-17   \n",
       "model_pow_39  1.26746e-13  4.90284e-14   3.4625e-15 -9.32913e-16 -1.11914e-16   \n",
       "\n",
       "                coef_x_35    coef_x_36    coef_x_37    coef_x_38   coef_x_39  \n",
       "model_pow_1           NaN          NaN          NaN          NaN         NaN  \n",
       "model_pow_2           NaN          NaN          NaN          NaN         NaN  \n",
       "model_pow_3           NaN          NaN          NaN          NaN         NaN  \n",
       "model_pow_4           NaN          NaN          NaN          NaN         NaN  \n",
       "model_pow_5           NaN          NaN          NaN          NaN         NaN  \n",
       "model_pow_6           NaN          NaN          NaN          NaN         NaN  \n",
       "model_pow_7           NaN          NaN          NaN          NaN         NaN  \n",
       "model_pow_8           NaN          NaN          NaN          NaN         NaN  \n",
       "model_pow_9           NaN          NaN          NaN          NaN         NaN  \n",
       "model_pow_10          NaN          NaN          NaN          NaN         NaN  \n",
       "model_pow_11          NaN          NaN          NaN          NaN         NaN  \n",
       "model_pow_12          NaN          NaN          NaN          NaN         NaN  \n",
       "model_pow_13          NaN          NaN          NaN          NaN         NaN  \n",
       "model_pow_14          NaN          NaN          NaN          NaN         NaN  \n",
       "model_pow_15          NaN          NaN          NaN          NaN         NaN  \n",
       "model_pow_16          NaN          NaN          NaN          NaN         NaN  \n",
       "model_pow_17          NaN          NaN          NaN          NaN         NaN  \n",
       "model_pow_18          NaN          NaN          NaN          NaN         NaN  \n",
       "model_pow_19          NaN          NaN          NaN          NaN         NaN  \n",
       "model_pow_20          NaN          NaN          NaN          NaN         NaN  \n",
       "model_pow_21          NaN          NaN          NaN          NaN         NaN  \n",
       "model_pow_22          NaN          NaN          NaN          NaN         NaN  \n",
       "model_pow_23          NaN          NaN          NaN          NaN         NaN  \n",
       "model_pow_24          NaN          NaN          NaN          NaN         NaN  \n",
       "model_pow_25          NaN          NaN          NaN          NaN         NaN  \n",
       "model_pow_26          NaN          NaN          NaN          NaN         NaN  \n",
       "model_pow_27          NaN          NaN          NaN          NaN         NaN  \n",
       "model_pow_28          NaN          NaN          NaN          NaN         NaN  \n",
       "model_pow_29          NaN          NaN          NaN          NaN         NaN  \n",
       "model_pow_30          NaN          NaN          NaN          NaN         NaN  \n",
       "model_pow_31          NaN          NaN          NaN          NaN         NaN  \n",
       "model_pow_32          NaN          NaN          NaN          NaN         NaN  \n",
       "model_pow_33          NaN          NaN          NaN          NaN         NaN  \n",
       "model_pow_34          NaN          NaN          NaN          NaN         NaN  \n",
       "model_pow_35  1.50451e-17          NaN          NaN          NaN         NaN  \n",
       "model_pow_36 -6.50458e-17  1.61389e-18          NaN          NaN         NaN  \n",
       "model_pow_37  3.05457e-17 -2.43645e-18   6.5338e-20          NaN         NaN  \n",
       "model_pow_38  2.06867e-17 -2.43159e-18   9.4776e-20 -9.61133e-22         NaN  \n",
       "model_pow_39  1.34592e-17  2.54811e-18 -4.62747e-19  2.59603e-20 -4.9882e-22  \n",
       "\n",
       "[39 rows x 42 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IhvFd/FyUB4HlwC8z9rcBuCbY1mv4oOy3GY//Ff9d9nTwHTroIk2Q+XAJ8F/47/hz8UuDxEO2LdMJwD3mq8r9Frg2SNv+M74a4N/wWRRbqDy960f4QgUv4AsUXJPned34IPMx/PvzC/yIGGY20XxlvKJVTgtxzq3DZ7R8Et/n3IDvc1Jznr8BXGS+ous3Q2wvju/DzsJ/Jt8GuoPPshQ536Nin7lz7m/4wk5Lgttv4OcR35kKUoO/uzPx88nWBvv4V3wwn5OZXWxmhUq1X4rvt/4bP4dvM/5vX+rI/PQTkfoJRgPWAhc555YUe34N27EGuMQ5F61XGxqd3iMRkcZkfpHVW5xzWjA8D71HUi0agZK6MLN3mF+HYjR+tGEAX0ZVRERERKRhKYCSejkFP/T9Cj417QLnXL4UPhERERGRhqAUPhERERERkZA0AiUiIiIiIhJSTdaB2mOPPdykSZNqsWkREWly999//yvOubxLFwwV9VUiIlJIvv6qJgHUpEmTWLZsWS02LSIiTc7Mnq3Rdj8BfAi/zs7DwBXOuS35nq++SkRECsnXXymFT0REmp6Z7Y9fz2WKc+4o/MLJ76tvq0REZDhSACUiIsNFG7CjmbXhF21eW+f2iIjIMKQASkRkBIrFYN48/+9w4Jx7Dvg6sAp4HnjdOXdH9vPMbKaZLTOzZS+//PJQN1NERIaBUAGUmX3CzB41s0fM7FYz26HWDRMRkdqIxaCrCz7/ef/vcAiizGw34HzgQGA/YGczuyT7ec65m5xzU5xzU/bcs+51LEREpJrWroXf/KbmuykaQCmvXERkeIlGIR6HRML/G43Wu0VVcQbwjHPuZefcVuCXwLQ6t0lERGrptdfgV7+CWbPg8MNh//3hggvgpZdqutuwVfhSeeVbUV65iEhT6+yE9nYfPLW3+9vDwCrgJDPbCdgMdAEqsSciMpy8+SYsXQq9vf5n+XJwDnbeGaZPhw99yKdW7LFHTZtRNIByzj1nZqm88s3AHfnyyoGZABMnTqx2O0VEpEoiEd/vRKM+eIpE6t2iyjnn7jGzXwDLgQHgAeCm+rZKREQqsnUr3HdfOmCKxfzVv1Gj4KST4Atf8AHTiSf6K4JDxJxzhZ/g88pvA94LrAf+F/iFc+6WfK+ZMmWK09oaIiLDyNatsGIFTJkCZhVtyszud85NqVLLyqa+SkSkwSST8PDD6YBp8WLYuNH3O8ce64Ol00+HU0/1o041lq+/CpPCty2vPNhQKq88bwAlIiJNrr8f7r3Xd159fXDXXbBpEzz5JBx8cL1bJyIiw4Fz8PTT6YBp0SJIVUg99FC49FIfNHV2QkdHXZuaKUwApbxyEZHhbvNmuPtuHyz19fnft2zxj02eDFdc4fPL99qrvu0UEZHm9sILPlhauND/++yz/v7994ezzvIjTF1dMH58fdtZQJg5UMorFxEZbjZu9KNKqYDp3nt9ml5LCxxzDHz4wzBjhk+TaKCrfiIi0mTWr/f9TGqU6bHH/P277QannQY33OADpkMPrThFfKiEqsLnnPsC8IUat0VERGrl9dd95aK+Pp+Wt2yZr2Pe2grHHw+zZ/uA6ZRTYJdd6t1aERFpVps3w513pkeYli3zc5t22slflLv8cj/KdMwxvg9qQmHLmIuISDNZtw6WLEkHTCtW+A5s1ChfrejTn/YB07RpMGZMvVsrIiLNamDAB0mpEaa77vLzaNvaYOpU+Nzn/AjT1KkwenTNmxNbHSO6MkrnpE4iE2pTZlYBlIjIcPDiiz5QShV9ePhhf/8OO/hSr5//vA+Ypk71VwFFRIapoTiBHtGcg0ceSY8w9fXBG2/4x445xi9qm6qUN3bskDYttjpGV08X8USc9tZ2ert7a/I3oABKRKQZrV2bnr/U1wd//SsAsdGdRCfMpnNmC5FLDvajTWVe8YvFhtdaUSIy/A3VCfSI88wz6RGmhQvhpZf8/YccAu9/vx9hOu20mi9gW0x0ZZR4Ik7CJYgn4kRXRhVAiYiMWM8+Ozhgeuopf//YsX7e0uWXE9v9HLquPZL4M0b7j6D3coiUmS0Ri/n+MB73axP29iqIEpHGN1Qn0MPeiy/6kuKpoOmZZ/z9++4LZ56ZrpQ3cWJ925mlc1In7a3t2wLozkmdNdmPAigRkUbjnA+QMgOmVav8Y7vt5tMiPvpRn5L3trf5PHMgOs8HPImE/zcaLT/oiUarty0RkaEyVCfQw84bbwyulPfII/7+XXbxI0vXXecDpre+taEr5UUmROjt7tUcKBGpLqVlNSDnfApequBDX59P0QPYc0+//tL11/uA6aijfKnxHDo7/WhRatSos7P8JmVvq6MD5s3T342INLahOoFuelu2+BOCVMB0333+itkOO/ishosv9gHTccc1VKW87Pltuea7RSZEav65K4ASGUGUltUgkkl/dS8VLC1enM4n33dfHyjNmOEDp8MPD321LxLxn2m+ALmU4DlzWx0dvsq5/m5EpBkMxQl0I8sMKsCnNXZOPJXIi+3pOUxLlxLbYwvRg4zOXY8kMmeOP0E46SQfRNWxzfk+u+z5bfPfMZ/Zf5pdl/luCqBERhClZdVJIgEPPphOx1uyBF591T82cSL8wz/4YGnGDD8ht4L0iEgk92daSfD8wAP6uxERaQaZQUartWLJJANugPYB6F0AkTXA5MnEPnYeXeN+TZwE7a1P0dt99rbgY6irGIYt/JE9v+22x26r23w3BVAiI0g1U7ykgK1bYfnydMC0dGm6xOvBB8P556dHmSZNGpImlRo8ZwZcra3bplnp70ZEpFE9+yzRX32d+NbNJAySiQQArgXibUZ0znuJXPQN2GsvokvmEV9023bBRz2qGBYq/JEZzGXPb7vwiAtZsmpJXea7KYASGUGKpXhJONulwvX3+/zxVMB0112waZN/8mGHwfvel07JGz++9u3JodTgOTPgArjqKj9Ypr8bEZEG8fLLgyvlPfUUneOh/TIfMLW2tGAtrQy4BO1t7XSeew3stReQv9hGPaoY5mtLrmAue37b5L0m12W+mwIokREmX4rXSFVqUQ0/MuOI9zvaWxL0Hv0JIo9930/IBV/k4fLL0wHT3nvXsPXhU/NKDZ6zA67ubv3diIjU1YYNfs5sagHbBx/0948b57+0r7mGyOmn0zvuDaLP9g0Kijp26iC6Mgqk54jlKrZRjyqG+dqSK5ibc+qcQYFSvea7KYASkREr9LygjRv9qNLixURvmUB885UkaCOeTBB9/jAiH/6wD5hOOaXsRQTLrY6YOVLU3w9z5/qffEFU2G1rtFJEpM76++Huu9MjTPfeCwMDfnH0k0+GL3/Zd2LHH5/OswYiQGTitEGbypWWlyv4qFcVw1xtaeSS9AqgRJqYSpJXJu+8oNdfhzvvTKfk3X+/77RaW+k89DLa264gnkzSPrqNzts+7nurEPJ9XtmB3Pz5sG5daSNF/f2+uN9f/uJrVFSjUp5GK0VEhlAi4av2pEaYliyBzZv90hUnnAA33OAXsJ02DXbcMfRmS03LG8pRnUIFKxq5JL0CKJEK1SuIuekmmDXLf9+OHq3S0uVIp6k52lsTdD74LTi+B1as8NHIqFG+0/rUp/wI07RpRMaOpTfjM4dw6yMVGu3KHkWaNcvvPky1vNRI0dy5PnhKJqtXKU8BuohIDTkHTzyRHmGKRuG11/xjRx7pJ592dfn+Z5ddyt5No47khClY0agl6RVAiVSgktLQlZycxmLwsY/5QRHwJ90qLV2Cl16CxYuJ9PXRu+9rRJ+eQGciSuTXD/g1MD73Od9hnXQS7LTTdi9PjcyE+fxvugluu81vJl8VvMz5Rmb+c3Uu/OcaifgAasmS6lVY1JphIiI1sHp1eoSptze9aPoBB8C73uW/eE8/HfbZp+BmSik13qgjOeUUrBjqEuv5KIASqUC56ypVenIajfqRhpTWVpWWLmjt2vSCtX198Pjj/v6ddiIybRqRK94KM74GJ57oh/NCKvb533QTXH11+vaoUf7f7AAnc77R+vXwta/5+5NJv4htGNWes9SMa4aZ2a7A94CjAAd80DkXq2+rRJpXo5ysZmvUduW0bh2xP/wP0Yd+S+dda4nE1vj799zTB0pdXf7nwAOLrgGYOu6OnTpKXkC23JGcsAvclvN5lDoyVo8S6/kogBKpQLnrKpUy8T/ffkeP9q9taYEbbyzt5Lbc0a9Cr6t3uteg/e/3bDpY6uuDJ5/0Txo71hd6uOwyXyHv+OP9B1emYp//bbcNvn3ssXDBBbnfo9So1rx5/jNNJv2/69aFb0815yw16Zph3wD+5Jy7yMzage2HD0UklEY6WW3EduUNGjZtSk9E7e0l9soDdHVDfGdoP6OF3nddQ+QfrvQVW1taStpf6rjNjKRLknTJmpYaj62OcdqC07a914suW7TdfsJ8Hvneq1JHxupRYj2fUAGUruqJ5Fbsqn++oKLSif+F9lsskInF/GNbt/oRkWqMmtU13cs5Yr94jq6L9yG+1Wi3OL3ufUS4G3bbDU49FVJV8o45ZlClokoV+/wvvBDuuCN9+8orYebMwttMBcfFApdaB6zNVoXPzMYB04HLAZxzcSBezzaJNLNGOllttHZtFzS87d+JLHvRf2nec4/vYNvbYdo0ohecQdwtJEGSuBnRk/YhcvTRJe8z87hbXAutLa0YVpM5TamA596199Kf6AegP9FPz4M9273XxT6PYgFWKSNjjTSXK+yZhK7qieSR76p/oaCiGhP/c+03TCDT0+MfB//v174Gv/pV8f0VSuka0nSv1KTb1OjS4sX0PPePbGEmjlbibhTRd/47vHtPomsOpvO0lrwBZqrtlQQIme9B5m1IB0u33eaDqWLBU+r1xYLynh64+WY/V6qWAWuTVeE7CHgZuNnM3gbcD1zrnNuU+SQzmwnMBJg4ceKQN1KkWTTCyWqukYu6tyuZJBr7CfGBLSRwxPs3E/3XjxK503xWw3XX+Y745JNhp53oXB2jvefOitubfdwfn/pxVjy/gguPuLCqAWRmwFNqu1pbWln1+ipiq2MF13Iqt72NNJeraAClq3oiuRUbASgWVFQ68T+1/46OdMnrcgKZ3/zGz9XJdXKfeYyFUroqSfcqOpKSTBL7yTNEf/4SnRt/T+TR7/kiEAD77EPsyA/xgxeuwiVaAEfb6DY6zp1G14e3DyQzA8zW1nTBhkqCkGJB68yZ4QKnTMWC8i1bfBwJzTM/aQi0AccBH3fO3WNm3wA+A3w+80nOuZuAmwCmTJnihryVIjVWrflB9T5ZzTdyMeTtcg7+/vd00YdFi+jc6VXaL4N4K7S3tNH54X+B317tsx6yVKu9mdvJnAO1ZNUSJu81ueztZv+9DBrpooVWayXpkoxqHUX327rztqvnwR5uXnEz313+XRY8uGDb51XtgLdRqvKFGYHSVT0Z8bJP8sOM9BQKKjK3V06aVGr/qRTAlhaf9jV/fvFAprsbvvtdH2SB7xtmzYLJk7dPBcw+xnxtLSXdK3sEaLv38cSEX129r4/Yr1+k5+5DuTn+AbZyAC1M4VtTD2Lml51PyTvkEKJfNRJRvy0zuOIKH1BmBpI9PX6fq1al708V4XDOByQ9PeUFIfmC1lx/M8Xen7BBeSp4Mmuq+Um1tgZY45y7J7j9C3wAJTJiVHt+UD1PVguNXNS8XWvXpju93l5YExR+mDABzjuPSFcXvUeOI7rx0dBV8KrR3tR25i2ZV5VRnVx/L9kBz/x3zGfdm+sKHmcq8BpIDmzXpnoH4rUSJoDSVT0Z0TKDlZYW+Na3tj9BzzUCkAoqenpyby8zaJgzp7Q2pU6kU0FAKgVw3brtA5nsk/JIBL79bfjIR9KvTyS2P4ZcgcGcOflP/sOke2Uf+2WX+TWYEgkjviVB9IpbiLxwLbz+OjFOossWssWNxmGAkcQxa/mVTD4SIm/x28wOVLuDC2Sp+1pb0+lura3pKVCtrT4Q2brV/3vzzf61YeeTZY4AZgetuRbGnT27cMBdalDe2gof/OD2bR6pnHMvmNlqMzvMOfcE0AU8Vu92iWSrZQW5RpgfVC1Dmqr32muwaJH/4l24EP76V39/R0e6Ut7pp8Mhh2yrlBcBIpxXu0nUZ+YAACAASURBVDYVUK33Jtffy5xT55QV8BRqU6OMGlVTmABKV/VkRItG0yM9yaQfrbnxxvApawsW+OctWJAOblLbK7TOT6GT9+wiFGb+p6NjcCCTnbKWOuFOpZRlLsTb0TF4QdhqVWHLPA4flKUDJn53O+2JLuKMot1tpXPzH+G974Xp04k+/E7iX98Rl8jcmm0X7OUb/Urdt2rV4BG3q66CiRP9c3t64Dvf8QHUwMDg7ZZSNGP+/HQaZaqSXmbwedttxQPuMOmXzVbYoQ4+Dvw4mKv7NHBFndsjMkitK8jVfX5QFVVr5CJnwPrmm7B0aXqEafly3xHsvLOv0PqhD/kv+aOPLqlS3lAp973Jfi/y/b2UE/AM15GmfIoGULqqJ7VS77LX2fK1J3sdnkQi90hPLrlOijs6Bo8c5Vrnp9hoRPa6QV//ug8AslPxMvefSPhgIRXIzZzpn5tqU64RkkpP1v1xOOL90N4ywPyDvkl74iPbAqbu0T+j+/89QbT9TDov3p/IO3+67XWrlg4u5w2+fxs9evtgLnv0KztNMBXEpkaoMp+b+VjmdgsFNNmPrVuXHkWMxXzQlhrpam/3BSSKzXULG7A2WWGHIeWcWwFMqXc7RLKlTlxXvb6qpiNEw+0kttKRi0EBq7XRm7yUyKK/+y/qeNyXok1NSO7qghNOqGhpi2oIO0KZ773J9/p8wXs1/16G40hTPmGr8OmqnlRVvcpe5wuS8rUnFvOBRWrOSWquUWY6XCG5Toqj0XRgYAYPPLD968KORkQifuHyVEC2devgqnqdnf5EPnO+U+b2MtceyrW/sk7WN22Cu+6Cvj6it0ygf/OVJGmjP2ms29BO73tuIjrq7T5gOusWfywZL8+e32Xmj+Gb3xw8ypPPTTcNHlkrd+5WoYCmo8N/hs4Nfix7xO+qq9IBWypYzdf+SMSPZKUq9ilIEhkeMk9cW1taaWtpgyQ1GyEaSSexeSWT8PDDRG//EvGtm0kYxAcSRKPfI7LpOLj2Wv9lfcopftSpQZQ7Qhlmgd186Z36eylPqABKV/Wk2sIECNVWKGjL157MSfstLXDGGaUteJvvBL2tLb3dXHNvSkmfW7u28G2XMSOxpSX39iqqoveXTURvWUNnopfIkz+CZcu2TTjqGP9FkrQCjiStdMz9OJGZgwOmbNnzu1LpdZmjPHnbEoOPfcw/H9IpkuXO3brsMnjhBdhnn8H7mD3b/620tPigJ9ffEfhUwcxUw2LFNVKjgEuWbF/UQ0SaU+aJK0m46rirmLjLxGExQhRWLed9Ab6jePrp9BWzhQvhlVfoHA/tl5uvltc2is5v/waOekf1918l5cxhC7vA7nBK72wE1VtRUqQE1ZpfU4pCQVu+9mTfX0rwlJJ94hyJ+LlI+ebepJ6TCrw6OnKvMZRy5ZVw772Db2cecyqYSMk84c+1v6Lpeq++6nPH+/qI/eFVup74FnEOpp0J9E5eQeRTp/kKedOmse7GsbR8Lp2Gt25dge0Gsud35Qv6colG04EX+FGgcv62clU5zJzDlgrwzAYfUyV/1/W4qCAitZd94tr9tu7tUquGS8pdLjWb9/X88z5Q6u0l9tDtRHd8kc6VEHH7w9lnQ1cXkdNPp9etrniu0FApJ8gJu8DucEvvrDcFUFIX9ZgMX+jkNl97atXO7u7Bc2+yCzik9g3FUx2zF2udPDm9rc7OdLpgSr4gJu8IyUsvwZIlxH76LNGlrXS+8DMixIiNms7cMf9Gv+1A0rUQb20l+v6biGSMEnV2+jS6UgKK7OAxTNpe9v5SFRNvvLG8zyx7FCxzoeNy/o7CqMdFBRGpvUInrrUuKtEIskdVeh7sKe8kfv16v3h6apTpMT8dP3b4WLre/SbxFqO9dTS93T8nMnHatpdFGF/Sfkr5TKodaJUT5JRSdlzpetWjAErqJrtaXDknnaW8rtjJbb4AolCBgnILK0Sj6cpt+Qo4QPhRidRirbnSFL/1rcFzgoqemD//vO+kUj+PP+5LitNLnNG0t32U+dc+zexvH0r/60bSpUaJLGdxh3ICinILJVQa8GaXJs81Clbu31Gt2y4ijSvfietwKTteKJDIPMFvbWnl5hU3M5AcKB4wbt4Md96ZDpjuv99/Ie+0E5x6Klx+OXR1Ed34R+LRL5BwjnhyK9Fn+wYFUKUK+5nUKvgtNcjRyFJ9KICSuiu3oEQ5r6u0glmlxS9yvb6c1MJ8cm1rzpwiBQxWrUoHS4sX+xXXAcaM8RNsu7uJPvt+4t/d0Zcfd63c9vBh20Zois0NG+qqceXuL19p8lyjYLU6JlXYExkewo5MhE3ZauQ0v2KBROYJ/qrXV/Hd5d/NHZwMDPg5tKmA6a67/FWstjaYOhU+9zn/JX3SSYMq5XWu7qd9yZerNrcn7GfSSMGvRpaGngIoqbty535UY85IqaNJle4z8/X9/T7ouPDC6qWE5Qu4tp2YOwdPPZ0Olvr6YOVK/6RddyV25Ifo6bgY9tuP7tm7EznVf0V0xqA9I+Uwuyx3OXPDqq3SkcHszzZM0QoRkWyljEyEGT1o9DS/MIFE6gQ/tjrGggcXpIOTxAR/taq31/dHGzb4FxxzjE+d6Oryo01jxuTdfy1KcYfZnooyjGwKoKTuyp37UemckXJGkyrdZ3aBhL/8xQci2QuxZiplVGK7gOskB399Ih0s9fXBc8/5J++xh18w8BOfgOnTiW2cTGdXK/G4f/jmP/iF2VP7zw7kipXlHkrFFr0N007NQRKRaih1ZKLY6EG9RjqqPYoGEBnYh95dryX62O10LllN5HOX+gcOOQQ+8AH/RX7aab5/KkG1R2DCbE+pcyObAiipu0rmyVQyZ6Sc0aRK95l6/dy5PnhKFSeo2mhHMklk7GNExvXBfwajTC++6B/bZx9fHW/6dP/v4YcPWmE9Os+vI5WS/Z7kqiZY78ApJd9nWUqQrDlIIlIN1R6ZqMdIR9VG0V58cVulPBYuhGeeIQJE9t0Xus6B67rg9NP9mg8F2tKoQYpS50YuBVDSECqZeF/uiW65Iw6VBg6RiA+gMlPgyh7tSCTgoYfSo0tLlsC6dcQ4iei48+k86QgiF+3vA6a3vMXX3SYYlfnXwYFCZ6dflD01AtVMozCZI3tmft4SlB4kN1JQKCLNqV4pZSnVCDjKHkV74w343e/S85geecQ/Yddd/Rf1ddf5q1pvfeu2/qjYsTRy+qKMXAqgpKZKnZdS6+dnyjXiUOk8mkr2nc+gNp0wAMuXpwOmpUvh9df9Ew88EM49l9j+F9H172cR32S0LzF650Lk0MHbyzUqE4n4/fT0+OdlL+7byCIRnwaZqjY4e7ZPMcwXJA/V5ywiI1M9UsqgegFH6FGvLVt8sYfUCNN99/kv4R128HOXLrnEjzAdd5xfmK9EjVSoQSSTAiipmVLnGMVi/oR261Y/ElJstKDSingweMQhc/HUlhZf/ju1xlItTrjDjHbEFm+l68wW4nGj3eL0jj6HyOaF/sFDD4X3vCedljdhAuBT8eJb84+6FBqVaeYRmHXrfEpk5ppNc+bkDpIr/bsREcml3ulm1Qo48o56JRK+nHhqhOnOO30Q1doKJ57ov3S7uvyX6ujRFR+PCjVIo1IAJTVTavpUT086fSwe97cLPb8aVfiyt5cq7pBM+tGMyZP9Y0N2wr1lC9x997aiD9HFJxMf+CcStBJ3bUSPvobIdVf7K3v77ptzE8VSE4drsYSiFQgD1f67ERGBxkg3q2bAEZkQITL+JHj8cfiv/0pfjUplPRx9NHz4w76DnD4dxo2ryjFkt0GFGqQRKYCSmqn1iXo1tx+L+eWQMlOyEwnfV0ANT7g3bfI7T6Xk3XMPxOPEiBDd9/10TN+L9qUQTzhaW9tYdez5xCZAJHfsBBRPDxyuxRLCHtdwDSBFpL4aId2sKgHHs8+mR5gWLoQXXvD3H3SQz3pIVcrba6/qNj4PFWqQRmTOuapvdMqUKW7ZsmVV3640n1JS32Ix/52cOrFNldAu9PxqzNfJTOky80slOeezD3p7/XOqNgL1xhs+5SEVMC1b5hcPbG31OeLTpxPb+wK6vnCyT9tr93N7HngAbr7ZP1VpZ5XTHKj6MrP7nXNT6t0O9VVSTY0wAlWWl1/2HW4qaHrqKX//3nv7+UtdXf5n0qS6NlOkHvL1VxqBkpoqdQ2jRYvCF1bIDGq6u8trXyzmK+KlUvdaW+Gqq3xF1cw2lD1i89prvjJeauHa5cv9jtra4IQT4Prr/Rymk0+GsWOBYA5T1oKuEyf64Kmao2AjOYho5rleIhLOUM9Happ0sw0bfH+UCpgeesjfP26c7xCuucZ3sEccEapSnshIpABKGkrqxDYWg3nz8p/cV2MeS2bRiGTSF45IBWNlL2b78su+Y0otXPvQQ+nhrKlT4R//0QdMJ50EO++ccxP5UsyqmXamQgoiMpyVOhpUrWArO90s33aHNLjr7/dza1MB0733+ityo0f7i3df/rLvEI4/3l/cE5Gi9D9FhlSYUY/sk/v58/0oTPaaRZUGFKkgLBU8nXGGH40qKZB4/vl0sNTXB4895u/fcUeYNg3++Z/95NqpU31Z1xDyzeWp5rwlFVIQkeGslPlItUq9y7fdmqf6JRI+7zsVMC1dCps3+47uhBPghht8JzttWuh+SUQGUwAlQybsqEfmyX1/v6+Gl0xuv2ZROQFFZgCXHYTNneufU2jki9Wr08FSXx/8/e/+/jFj4JRTiE3/NFE3g84P7Edk+qiS3p9MuUa8qpl2pkIKMlyZWSuwDHjOOffOerdH6qOUanS1Kv6Qb7tV359z8MQT6YApGvXp4wBHHunz0ru6fPbDLrtUfFwiUkIApU5JMmUGIhCumEPYUY/Mk3sz//zMtX3yrVlUbHQrVwCXGYRB1uN/cUT2eWZwwLRypX/iLrv4UuIzZ/pO6dhjid3Xln59T2OnxQ3XSnwiwLXA40D1aypL0yhlPlKt1hrKt92q7G/16sGV8tau9fdPmgT/7//5zuz0030hCBGpulJGoNQpCTA4EGlt9QFOIuEfu/nmwdXzUkFNR4cvE55Kry406pF5ct/RAbNnFx8pCTO6lSuAmzMn/bx5X3HE+yGRNOJbEkTP+jcib8zxD3Z0+FS82bN9wDR58narqjdbWpwKKchwY2bjgXOALwPX1bk5Umdhy1/XqvhDvu2Wtb916wZXyktlP+y55+BKeQcdVJW2i0hhoQIodUqSKTNQSAVOKZmBQ64iDW1tPpugWNnxzJP7yZOLj5SECV62S1ubnoRHHttWIa/zL5toT/6cOKNodwN0TtkIF37LB0yHH+4PoAClxQ1/I7lyYZOYD9wAjK13Q6S51GqtoXzbLbq/jRt9BddUwPTggz5Vb+xY3yd95CO+gz3qqKJ9k4hUX9gRqKKdkpnNBGYCTJw4sfKWScPKDBRgcBDV0pIOHDKLNID/N5HwJbkLFZDIPkHNDKbyncCGCV4iJybo/faTRH/xCp0bfkfk/O/5q3oA48cTOWsGveP/j2h8Gp0X7UFk2pdKel+UFje8qXJhYzOzdwIvOefuN7POAs9TXyU5DXXZ80Hicb+Qeipguuce2LrVf9lMmwZf/KL/ApoyBUaVP79WRKqjaAAVtlNyzt0E3AR+ccKqtVAaTnaK3TXX+FGm1lb49re3r5SXXSa83DS8Qo/nDF4GBnwlotT8paVLiaxfTwTgwAPh3HN9Wt6MGf62GRGgkm5TaXHDV7OlaI5AJwPnmdnZwA7AODO7xTl3SeaT1FdJLkO+CG4yCStW+PlLvb1+tGnTJt9RHn88XHed7/BOPhl22ql27RCRsoQZgQrVKcnIkjpxjEbhm9/cvsx46jmZgVau52QqdoJa7PHI8XEibhlE++Cf++DOO30aBMChh8JFF/lgacYMmDChOm+EjBhK0Wxszrk5wByA4GLf9eqnhp9ajRLVqhLfNs75eUupEaZFi+DVV/1jhx8OV1yRrpS3227V26+I1ETRAEqdkuQSi/kTyFQhicyRp0yljMgUO0Hd7vFIP/TdvW0OE3fd5de6AL+C+qWX+s5o+nTYd9/yD1YEpWiK1FstR4lqUonvuefSI0y9vbBmjb9/wgQ477x0pbz99tv2ktjqGNFH6pRGKCKhaR2oJtMok9h7egbPgfroR32xh0raVOwENXL0Jnq/+hjRX6+n87VfEXnHD3x+oBkcfbSvTjFjhi8vvuee5TdEJA+laDYH51wUiNa5GVJltRwlqkolvldf9R1YKmB64gl/f0fH4Ep5Bx/s+60sQ55GKCJlKymAUqdUX408iT2R8AvRzp1bvOBDIYNOUN94w48qpeYw3XcfkYEBIi0tcNxxfoXdGTPglFOU8iAiMszVar2mlJIr8b35Jixdmg6Yli/3qXo77+z7ptQCtkcfHapSXs3TCEWkajQC1UQaaRJ7dzd8//u+SFDKX/6SrroK4YK9QUHWW1/znVEqYFq+3E+0bWuDE06A66/36XgnnwzjtByZiMhIUqv1mkLbuhXuvTcdMMVi/r5Ro3wHN3eu7/hOPLGsSnm1DhBFpHoUQDWRRprEHonAjTf61L1UGfNkMh3YQfFgL/bH9XRdMIb4VqOdOL3uHCLE/MFNnQqf/ay/iheJ+Ct6IiLS1CotAlGr9ZpySibh4YfTAdPixb4wkZnPgpg9m9iJ+xLd/Q0633Jmxe2qe4AoIqEpgGoi1Z7EXul8qtQSSpnMfLr35Mk5gr0XXkiPLi1eTPTRc4nzLyRoJc4ool3/QuTzbT542mGHyg5OREQaSiPM8SkYwDkHTz01uFLeK6/4xw47zKdedHX5Dm333QcfT+xfq3I8QxogikjZFEA1mbCT2IsFR7nmU0FpAVXmiJiZ73uSSZg9O+h/fvIi0Z+spbP/z0Quvxn+9jf/wjFj4OST6Tz1cNpvNuIDjvb2Njr/pauyRZhERKRh1XuOT84Arm3S4Ep5q1b5J++/P5x9drpS3vjxDXc8IlI/CqCGoTDFJrLnU/X0wIIFpRWoyBwRW/Ws47vfhWTSiG9JED3nP5jz2g0+HtplF18Z76qr/Bym446DtjYiQG93Y1QVFBGR2qrlHJ8wqYGDAp6tW4jOvoDIL1/yD+6+O5x2GnzmMz5gOvTQnJXyhup4RKSxKYAahsIUm8ieTwUlFqgIFgWMPNJH5NE+YndsYEHyVuKMot1tpXPyOnjXf/o5TEcf7ReLytIoJdlFRKT2ajXHJ7Y6xmkLTtsWyCy6bFF625s3+0XVe3vpvP83tJ+YIN4C7UlHpx0IX7veX3E85phQlfKG4nhEpPEpgBqGwhSbyJ5PBYNHoLZ7jXPw2GOD5jDxwgv+sb33JtI5nd4JtxNNnELnu/cicvJXC7axkUuyi4hIbdRijk/Pgz30J/oB6E/003PH14i8MMV3LHfd5dcLbGsjctJJ9LZ9kOjBrXSefAmMGsW8lVE69+z3y2OUQXOWREYmBVDDUNhiE9nzqQa9ZmoSVjyUDpiWLElPpt1/f5/iMGOG/wlSHSKEn8LUSCXZRUSkSTkHr2ZUNHLAr34Nf/i1H1WaNctfrTv1VBgzZls/1QgFLUSkeSmAamKFUuDCFpvYZmCASNsDREb1wbzFPmBav94/NmkSnHNOOmA68MCiueHFNFJJdhERaSJPP50u/LBwId3tL/GDy2FrC4yyFrrf/UX44dWwxx55N6ECECJSCQVQTariFLh4HJYt86l4fX0+R3zDBv/YW94CF13kg6Xp02HixKq3v9ol2UVEZJh68cXBlfJWrvT377svnHkmka4uokftQnTzX0PPRVIBCBGphAKoJlVyCtyWLX4F9SAlL7Y0QbT/JDqJEjniDbjkEh8sTZ8O++03JMdQ8iiZiIgMf6+/7i/upQKmRx7x9++6q6+U98lPEntbB1GeoXPSaX4eEqWtgqECECJSCQVQTapoCtybb/phqtQcpnvu8RNpzYgddDFdie8Tb2mjvd3o/Z4pkBERkfrYssUXe0gFTMuW+auDO+4Ip5ziL/B1dcGxx0Jra9XmL6kAhIiUSwFUg8g1n6nYHKdBKXBHbYA/3UnsJ88Q7YPO535CJLHUl2U99lj42Md8St4ppxD9zu7EPw+JJMS3qoCDiIgMoUQC7r8/HTDdeacPolpb4cQTYc4cHzBFIjB69HYv1/wlEak3BVANINd8Jsg9x2lbUHX8BiL9USKvLoZr+2D5cmKJE+iilzjttLddSe+/30/kQ0fCuHGD9qcCDiIiMmRSy2AERR+IRn2aHvh1Aj/ykXSlvKz+KltsdYxVr6+iraUNkmj+kojUhQKoBtDT4y++OZeezwRZc5z+sAn6lnHa56YRT7TSzigW8RUi7cth6lSYM4foc93Ee3YkkTDiDqL9ESI5+iIVcBARkWqKrY4Nnk/07LPpEaaFC9PrBh50ELznPT5gOu002GuvkvaRSt1rbWnlquOuovtt3Rp9EpEhpwCqzmIx+MEPfPAE0NYWjAitW0d76y7Ek0Z7Mk7nl8+gh276mQ4Y/Yym59xfEPnZ7j5PHOiMQftPC48sZaYFzpkzJIcoIiLDmA9sTic+EKc9afT+eR8i9zznH9x7bx8sdXX59QMnTSp7P5mpeyRh4i4TFTyJSF0ogKqzaNSPMgGYJbnioMVELr8a/vY3ejmJ6Kgz6Tz2dSLnn0vPvRfAb1KvNL+g7Y7pbRUbWaq49LmIiAj4ZS+CSnnR1bcSP2ILiRaIA9GjxxH5wA2+wzniiFDrBm43gpWDSo+LSKMoGkCZ2QSgB9gHSAI3Oee+UeuGDWvO+XUs+vroXLqG9uQnidNGu9tK97NfInbMuUQPOo/Oi/ZgzmWH+mEpoDsGP/gjbN0Ko0ZBd/f2my5UGrzk0uciIiLgq7jGYumUvHvvhYEBGD2azrMm09byMkmStI1qp+Pq2cx7cx2d494gEjJ4ClNVT6XHRaRRhBmBGgA+6ZxbbmZjgfvN7P+cc4/VuG3Dh3Pw97/7cuKphWtXrwYgsvvu9J66kejYc+m8aA845M90ndnqR4n6oPeIdJATifigp9y5SyoeISLDlS72VVkiAQ88kJ7HtHQpbN7sK7uecALcEIwwTZsGLz+AW9AJiSQJl+CaP17DQHIgdInxUqrqqfS4iDSCogGUc+554Png9w1m9jiwPzBsA6hC5cNDSVUcSgVLixfD88/7x/bay5cT//Sn/aK1Rx5JpKVl2wKAH/nI9gUlMttQyeKzKh4hIsOYLvZVwjn461/TI0yLFsH69f6xo46CmTP9HKYZM2CXXQa9NLoySiKZwOFIJBMk8L+HLTGu1DwRaTYlzYEys0nAscA9OR6bCcwEmDhxYhWaVh9lzRNKJuHhh9OL1i5eDK+84h/bf39faWj6dN/xHHbYoHzwzGAN8hSUqKJKAjARkUY1Ei/2VWz16sGV8tau9fdPmgQXXpgu/LD33gU3kxkAtba0Yti2EagwwZBS80Sk2YQOoMxsDHAbMNs590b24865m4CbAKZMmeKq1sIhFmqe0MAArFiRDpiWLElfqTvgADj7bB8szZjhS7bmyQHPDtYuuyyzoARccYWCHRGRUo2Ei31leeUVP7K0cKEPmv7+d3//nnv6QClVLe+gg0rabHYABJQcDCk1T0SaSagAysxG4YOnHzvnflnbJtVXznlCW7fCsmXpgOnOO30FIoBDDvFX6mbM8KNMBxwQel/ZwRoM3neuIhEiIpLfSLnYF8rGjf4CX2qU6cEHfYrD2LG+z/roR33AdOSRfm5TBbIDIAVDIjKchanCZ8D3gcedc/9R+ybVVyQCvbf3E/3JWjqJEvmnn8Bdd8Gbb/onHH44XHxxOmDab7+y95UdrHV3+x/NURIRKd1IutiXUzwO99yTDpjuvttnTLS3+2IPX/yiD5imTPGlXEVEpCxhRqBOBi4FHjazFcF9n3XO3V67Zg2xN9/0HU0wwhS5+24i/f3+saOPhiuv9MHS9OklrZoOhQtS5CvqoMBJRKQ0I+1iH+Dn365YkQ6Ylizx/VlLCxx/PFx/fbpS3k471bu1IiLDRpgqfEuB4gs5NJMNG/yoUiol7777fJpeSwsce6xPa5gxA049FXbfvezdZM5xam2FD37QjzBVq6qeiIhsM/wv9qWWxEgFTIsWwauv+scOP9x3Ml1d/orcrruWtYswC9qKiIx0JVXha1rr18PSpcRuXUm0z+h8/lYiyTt9mbspU+ATn/AB08knb1eeNVOp5c0z5zglEvA//+Or7GkBWxGR6hqWF/sAnntucKW8NWv8/RMnwvnnpyvl7btvxbsKu6CtiMhINzwDqHXrBq/BtGIFMTeVLnqJ005721X0/scKIlceAWPGhNpkdsW8+fP9bgoFU6k5Tps3p++Lx6GnRwGUiIjk8Oqr/ipbKmh64gl/f0fH4Ep5Bx+ct8JruUpZ0FZEZCQbHgHUiy8S++4jRP+wic6Xfk7k6R/7+3fYwUcqX/gC0TWXEr95RxIJI+4g+uaJRArETtmjTZmjSf39MGuWTz8vtFZUao7T7Nlw7701OG4REWlumzbB0qXpEably32q3s47+8yImTN9wDR5csWV8orRgrYiIuE0ZwC1Zk16hKmvj9gTu6VHl1rOpPfqtxO55GA44QQYPRqAzhi0/zirPHkeuRbTzayYZ+YDqWSywFpRgUjEj1addtrganvZC+jWovJeqSmHIiJSY1u3+itqqRGmWMzfN2qU/6KeO9d3QCeeOOSV8rSgrYhIOM0RQK1cmS740NcHTz/t7x83Dk49leiBnyJ+x44kkkbc2ogecBmRUwZvIl/Fu1xyLaY7Z0769R0dflQpTDCW2veiRYMDpsziEmbpSrNhUgPDyBUEKogSERliySQ89FB6hGnxYr8+kxkcd5yfg9vV5efg7rxzTZsSpkCEFrQVESmuMQOoF1+E3/0uPYdp1Sp//+67+8p4s2b51Ia31zu1jAAAIABJREFUvQ1aW+mMQetCSG71AUm+gCZXxbtcozS5FtPNft7kyaWN7mTue968dICWTPr7nAufGhhGriBQAZSISI05B089NbhS3iuv+McOO8ynIKQq5VVQ5bVUKhAhIlI9jRlArVgBV13l11yaPh0+9SkfMBVYLT01l7aUObWFRmkuu8z/293t/831vHIDkswALXMEqpTUwFL2EWaUTEREyvT88350KRU0pS767b8/nH12ulLe+PF1a6IKRIiIVE9jBlDTp8Njj8Fb3xoqIopGfQDinP83bOCRa5QGBgdL3d3VG83JHMXKTCdMtaXU1MBCSklZFBGRMiQSfsHaBx/0t3ff3U94/cxnfEfylrdUvVJeuVQgQkSkehozgNpxR78oYEjljrbkel2uYKkaozm5RrvmzEk/ngpwSk0NLESL9IqI1FBrK7z97XDxxf4L/phjal4pr1wqECEiUj2NGUCVKNdoS5gKdPlGabKDpWqM5oQdxVLQIyLSRP7t3+rdgtBUIEJEpDqaOoDKDpJSgUcpFehS96fS9/IFS7meV0qZcM1JEhERERFpfk0bQBUKkkqZs5RvO7mq9WU+b/78wfOVilXM05wkEREREZHm15jJ2iHkKwAB6dGe1tbioz2FtlPoebfdFu51mSIRP+9JwZOIiAy12OoY85bMI7Y6Vu+miIg0taYYgQq7VlNKJOJHiG67DS68sHDAkm872fvMft6FF8KSJUrJExGR3MIsXDtUbejYqYPZf5qtdaBERKqg4QOoQil2+VLiYrF0et2SJb6yXSmFJPLtM/t51ayYJyIiw0e1Fq6tJAjLbIOZkXRJki6pdaBERCrU8AFUoflM2XOVUqNGq1aVtm5TdgGKuXOhv3/7BW2z96eKeSIikks1Fq6tNAjLbEOLa6G1pRXDaG9tp2OnDuYtmaeS5iIiZWj4ACps9brUqFF/v1+3MLUUR67X5Kuel7mNZNJvp61N6XkiIlKaMAvXFhtdqjQIy27D/HfMZ92b65TOJyJSoVABlJm9A/gG0Ap8zzn31Zq2KkPY6nXRaDrwSbnqKujuzh0kFarel9qGc/5HRESkFMUWrg0zuhQmCCunDfOWzKt4dExEZCQrGkCZWSvwLeDtwBrgPjP7rXPusVo3LiVfql52UYmWlsHBz8SJ2wdchVICU6NdW7akA6dEongKoIiISLZUUBJdGR10O3VfsSCmWBAWtg3VDsxEREa6MCNQJwJPOueeBjCznwLnA0MWQGUqVFTiW9+CWbN80DN6dO7Uu2LV+3p7oacHbr4ZBgZUYU9ERMpTaJQpbBCTKwCqVDUCMxGRkSxMALU/sDrj9hpgavaTzGwmMBNg4sSJVWlcLoVGkGbOLF4Zr1hKYCoY6+5WhT0RkWZSz3TzXAqNMqWCmJ4He+rStloEZiIiI0WYAMpy3LfdzCDn3E3ATQBTpkyp2cyhYkUlMuczZd7Ofk6xoEgV9kREmkcjpJtnCzPKtODBBcQTcRY8uEDFHEREmkSYAGoNMCHj9nhgbW2aU1yxEaRCRSJERGTYaqh0cyieKleNUuciIjL0wgRQ9wFvMbMDgeeA9wEfqGmriig0OlQoxU9ERIathko3TymUKpdrhKqShXNFRGRoFA2gnHMDZjYL+DM+r/wHzrlHa96yPPKt4ZQSdt0oEREZVhoq3TyM7BEqoKKFc0VEZGiEWgfKOXc7cHuN21JUmPS8sOtGiYjIsNJQ6eZhZY5QaX0mEZHmECqAahRh0/NUAEJEZMRpuHTzUml9JhGR5tBUAZTS80REJJdGSzcvh9ZnEhFpDk0RQGXOe8qXnldsbpSIiAxvjZJuXgmtzyQi0vgaPoDKnvc0f37x56h0uYiIiIiI1ELDB1CZ8576+2HWLEgmBwdKKl0uIiIiIiJDoaXeDSgmNe+ptRVaWnyQlBkoZT9Hc6NERERERKRWGn4EKrMseUcHzJ69fREJlS4XEREREZGh0PABFAwuSz55cu5ASaXLRURERESk1hoygCpUUU+BkoiINJrY6pjKj4uIjBANF0Cpop6IiDST2OoYXT1d2xbA7e3uVRAlIjKMNVwRiVwV9URERBpVdGWUeCJOwiWIJ+JEV0br3SQREamhhgugVFFPRESaSeekTtpb22m1Vtpb2+mc1FnvJomISA01XAqfKuqJiEgziUyI0NvdqzlQIiIjRMMFUKBCESIi0lwiEyIKnERERoiGS+ETERERERFpVAqgREREREREQjLnXPU3avYy8GwFm9gDeKVKzWkEOp7GNtyOB4bfMel4Glupx3OAc27PWjUmLPVV2xluxwPD75h0PI1Nx9PYyjmenP1VTQKoSpnZMufclHq3o1p0PI1tuB0PDL9j0vE0tuF2PGENt+MebscDw++YdDyNTcfT2Kp5PErhExERERERCUkBlIiIiIiISEiNGkDdVO8GVJmOp7ENt+OB4XdMOp7GNtyOJ6zhdtzD7Xhg+B2Tjqex6XgaW9WOpyHnQImIiIiIiDSiRh2BEhERERERaTgKoEREREREREJquADKzN5hZk+Y2ZNm9pl6t6cSZvYDM3vJzB6pd1uqwcwmmNkiM3vczB41s2vr3aZKmNkOZnavmT0YHM8/17tN1WBmrWb2gJn9vt5tqZSZrTSzh81shZktq3d7KmVmu5rZL8zsr8H/o0i921QuMzss+FxSP2+Y2ex6t2uoqK9qXOqrmoP6qsalvirEdhtpDpSZtQJ/A94OrAHuA97vnHusrg0rk5lNBzYCPc65o+rdnkqZ2b7Avs655WY2FrgfuKCJPx8DdnbObTSzUcBS4Frn3N11blpFzOw6YAowzjn3znq3pxJmthKY4pwbFgv5mdkCYIlz7ntm1g7s5JxbX+92VSr47n4OmOqcq2Rh2qagvqqxqa9qDuqrGpf6quIabQTqROBJ59zTzrk48FPg/Dq3qWzOucXAq/VuR7U45553zi0Pft8APA7sX99Wlc95G4Obo4KfxrmiUAYzGw+cA3yv3m2RwcxsHDAd+D6Acy4+HDqkQBfw1EgIngLqqxqY+qrGp76qcamvCqfRAqj9gdUZt9fQxF96w5mZTQKOBe6pb0sqE6QQrABeAv7POdfUxwPMB24AkvVuSJU44A4zu9/MZta7MRU6CHgZuDlIW/meme1c70ZVyfuAW+vdiCGkvqpJqK9qWOqrGpf6qhAaLYCyHPc19VWW4cjMxgC3AbOdc2/Uuz2VcM4lnHPHAOOBE82sadNXzOydwEvOufvr3ZYqOtk5dxxwFvCxINWoWbUBxwH/7Zw7FtgENPXcGYAgveM84H/r3ZYhpL6qCaivakzqqxqe+qoQGi2AWgNMyLg9Hlhbp7ZIDkH+9W3Aj51zv6x3e6olGJ6OAu+oc1MqcTJwXpCL/VPgdDO7pb5Nqoxzbm3w70vAr/CpU81qDbAm48rxL/CdVLM7C1junHux3g0ZQuqrGpz6qoamvqqxqa8KodECqPuAt5jZgUGk+D7gt3VukwSCiazfBx53zv1HvdtTKTPb08x2DX7fETgD+Gt9W1U+59wc59x459wk/P+dhc65S+rcrLKZ2c7BBHCC9IEzgaatEuacewFYbWaHBXd1AU05qT3L+xlZ6Xugvqqhqa9qbOqrGpv6qnDaqrWhanDODZjZLODPQCvwA+fco3VuVtnM7FagE9jDzNYAX3DOfb++rarIycClwMNBLjbAZ51zt9exTZXYF1gQVGVpAX7unGv6cqrDyN7Ar/y5EG3AT5xzf6pvkyr2ceDHwUn308AVdW5PRcxsJ3wluqvr3ZahpL6q4amvkqGkvqrB1aKvaqgy5iIiIiIiIo2s0VL4REREREREGpYCKBERERERkZAUQImIiIiIiISkAEpERERERCQkBVAiIiIiIiIhKYASEREREREJSQGUiIiIiIhISAqgREREREREQlIAJSIiIiIiEpICKBERERERkZAUQImIiIiIiISkAEryMrNWM9toZhOr+dxmZGZnmNnKIdjPl8zsh1Xc3iwzeyn4bHap1nZFRIaCmU0yM2dmbcHtP5rZZWGeW8a+Pmtm36ukvY2q0vemhP1cbmZLq7i9d5nZ6qAPO7Za2xWplAKoYST4gkn9JM1sc8bti0vdnnMu4Zwb45xbVc3njnRmdouZxYPP5VUzu8PMDi1jO2vMrLPA4zsAXwdOCz6b1ytodlPKCOwzfxJm9p/1bpvISGBmfzazL+a4/3wze6HUE3rn3FnOuQVVaFenma3J2vZXnHMfqnTbw52ZzTWzrcH36Xozu8vMImVsJ2pmxd7vrwOzgj7sgfJa3NzM7NGsPmzAzH5X73aNdAqghpHgC2aMc24MsAo4N+O+H2c/v9ZXoqSgrwSf0wTgVeAHNdjHPsBo59yjpb7QzFrMrKG/H8K0MSOwT/2/2A/oB/53SBopIj8ELjUzy7r/UuDHzrmBoW+SVMHPgu/UPYGlwC9zfMbVcABQch8G/gJaldtSdWHa6Jw7MqMPG4s/v1MfVmcNfYIk1RWkh/3MzG41sw3AJWYWMbO7g6tIz5vZN81sVPD8tmDI//+zd+fhUVXnA8e/70wyAQREgygiiGhttVKrxuVqsaOh7lUUbbXVuKNWrbj+pIrSWhtcWtFqLbhQImpdUGstbkRGo05VQNyLCyLgbhRElkwyc35/nDvJZDJ7ZjKT5P08zzzMcufOuXfC3Pve95z3jHQfz3Zff1xE1ohIUES2yXZZ9/WDReRdEVktIn8VkRdE5KQk7d5LRBaJyLci8rmIXOc+7xGRB92rmKvcq1k7xLxvtojc7F4B/U5EnhORzd3PWyUi74jIzjHLrxSR/3Of/0ZE7hCRiiRt2kpEHhaRL0XkQxE5O5fvxBizFrgX2CnJ54xzrz6tEpFnROT77vP3YoOBx91tuyDufTvgHnTc159y7/9ERBa4+/1lEdkz5j3Pi8hVIhIE1gIdumOm20cicqaIvC8ijSLyiIgMdZ+/Wtysj4hUiMgGEfmT+7i/+3hj9/E+MX+Ti0Vk32zamMYxwMfGmBezfJ9SKjePAJsCY6JPiMgmwGFAnfv4UBF51f2NXyEiU5KtTGKyFmIzzNeLyFcishQ4NG7Zk93fqjUislREznCf3wh4HNhS2q7qbyk2szI75v2Hx/z+xh9flonIRSLyuvt7ep/YrH+iNm8nIs+6y30lIvfFvHaju83fishCEYndT1NE5AH3WLZGRN4Qke1FZJLYrtkrROSAuH1T6/62rxaRf4nIpknatLH7+/2piHws9vwg64DDGNMMzMJesKtM8Dl7i8grbnteEZG93eevxv5N3Ozu/5vj3lchIt8BXuA1EfnAfX4HdztXud/N4THv+YeI3Coic0VkLbBfgvak3EfJvnP3b+nfMcu9LyL3xzxeISI/du//QESeFtvDZImI/CKbNqaxLzAEmJPl+1S+GWP01gNvwDJgbNxzfwRCwM+xwXNfYHdgT6AMGAW8i02X4z5ngJHu49nAV0AVUA7cB8zOYdkhwBrgCPe1C4Bm4KQk2/IKcJx7fwCwp3vfA5zkPtcHuBlYEPO+2cAXwC7u688CHwK/wv4oTwWejll+JfA6sBUwGPgvMMV9bSywzL3vBRYDvwN8wHbu/q52X/8p8FWK72Z2zHoHuPtmfsx39A/3/g7Ad8D+7n76nfv9lMe015/ic7az/8VbHw8GVgPHud/X8UAjsIn7+vPuduzgfl5ZgnWm2kcHuPv7x+7+/hvwTMxrr7r39wU+AF6IeW2he3+426YD3e/3IOzfUWWyNgKXAY9k+P/iOeDyYv//1JveetMNuA24PebxGcDimMd+YLT7f/5HwOfAOPe1kdhjS5n7OACc5t4/E/if+7uxKTA/btlDgW0BcX+X1wG7xnzmyrh2TqHtOLU99iLNz9zfmkuA9wGf+/oy4GXshaxNgXeAM5Ns/73u75TH/W38Scxrx2MDjzLgQuAzoE9Meza4v4dl2IDzQ3dd5cDpwIcx6woAH2MvyG2EPcmenWQ/PgJMd5cb4m7LGe5rI4BVwIgk2xO7nyqA64AV7uOTgOfd+5sC32CzjWXYY883tP2et36XKf52DLCde7/c/Q6ix979secS33df/wf2GLdPdF8nWF+qfZT0O8eeH61y1zsU+Ah7MQ73tW/c1zYCVgAnu9u8K/YY9sNkbcSek7ye4f+lO3HPEfRW5N+1YjdAbwX6YpMHUM+ked9FwAPu/URB0d9jlj0ceDOHZU8BGmJeE+BTkgdQLwJXRH90U7R9sNuGjWLacGvM6+cDb8Q83oWYQAcbHJwW1+Yl7v3YAGofYGncZ08Gbsvwu5mNPSiucrf7EWCbmO/oH+793wP3xLzPgz24/iSmvf4UnxMfQJ0MvBi3zCvA8e7954Er0rQ91T6ahe2aGH1tIBDGBlsbYbvODQIuB/4P+AToB1wN/MV9z2XAzLjPrAd+nWkbU7R9lNuehCcFetOb3gpzA36CPWns6z5+ATg/xfLTgBvc+yNJHkA9Q0zQgr0Y07psgvU+Apzn3veTOoCaDNwf85oHe+Ltdx8vi/52uo+vJeaYF7feOmAGsFUG++obYOeY9sRe5Ps59qKa1308wN3eQTH7ZmrM8jtiL5p6Y/cjsLn7e9w3ZtnjcC/kZdDGKe56V2Evmj0D7Oa+dhJtAdQJwMtx7w3iHuvJPoAagz0GemJev5e2i3j/AOrSrC/VPkr3na/ABkTHut/ny8APsMfWR91lfknM+Y373HTgykzbmKLt/YBvSXHc11vX3bQLX++zIvaBm2r+j9hucN8Cf8AGIsl8FnN/HdA/h2W3jG2Hsb8M7QbzxjkZ+yO3xE27H+K23Ssi17pdM77FXikirv2fx9xfn+BxfPtj989HblvjbQ2McFP8q0RkFfZK1RYptiHeVGPMIGPMUGPMOGPMhwmW2dJtAwDGmAh2Pw3L4nOSrs/1Udz6VpBesn0U395vsScDw4ztqvgqNvu0L/Yg9l/AwV4ZftZ929bAcXH7di/afw+ZtDGRGiBgtNCJUl3KGPM88CVwhIiMwvZ8uCf6uojsKSLzxXaJXo3NLKU6DkW1O5YQ9/smtqv4f92uVKuAQzJcb3Td8b+/K2j/e5np8fAS7IXCl93uYafEtPFCsd0MV7tt3JjUx7CvjDHhmMfEfW78/iin4zZv7T7/aczv7HRsJipT97vHsCHGmP2NMQsTLJPJMScbW2IzXZEU68vlGBbdR+m+82exgfe+7v0A9vgVfwzbM+4Y9mvanx/kegw7Cjtm+tl0C6rC0wCq9zFxj6cDb2Kv8AzEZnoKMRA01qfYrAQAIiKk+EE1xiwxxhyL/XH/MzDH7Wtegz0g7o896GwXXWUn2jY85v4IbJYk3grgPffgEb0NMMb8vBOfm8gn2B9jwI75wu63j92n4r/LrNbnGhGzvkzXmWwfxbd3ALBJzPqfxWbyRgML3ccHA7sBDe4yK7AZqNh9u5Ex5ros29iO+zd2AjZLppTqenXY3+wTgKeMMbGBwT3Ao8BwY8zGwN/J7Hf8Uzr+HgF2DA22e9b1wObGmEHA3Jj1pvsdif89E/ezPk76jiSMMZ8ZY043xmyJ7b74N3dc1BhsNv4X2K7Ug7CZunwew5qxXchircBmoAbH/M4ONMb8sBOfm0i6Y04ux7Dh0r54UD6OYdF9lO47jwZQY9z7z9IxgFoBPBt3DOtvjDkryzYmciI2e5Xr+1UeaQClBmB/sNe6gyXP6ILPfAzYVUR+LrYS4HnYSj4JicgJIjLYvRq0GvvjE8G2vQk7ZibaFayzzhGRYSJSCUzCjk+KFwRC7pXDPm4mbLSI7JaHz491P3C42HK75cDF2P7eL7mvf47tlpapx4AfisgvxRb9+BU26JybZbuS7aN7gVNF5EfuyUsttitDNLv4LLZ7x+vGVt4KABOAd40xX7vL3AUcKSI/c/drHxHZT0QSZQKzMQYdeKtUMdVhL6CcTscLGQOAr40xG0RkD+yYkEzcD/xWbFGfTYBLY17zYcfnfAm0iMjB2C5+UZ8DlZJ8frz7gUNFpNr9/b0Qe7zJugCNiBwjItGLht9gj2Fh7Ha3uG0sE5ErsF2fO+N4EdlRRPphe5Q8GJOxAsAY8ynwFPBnERkotiDTtiLy005+dry5wPYi8iv3mPNLbG+Sx9zXsz2GvYQdo3SJiJSLncbj58A/s2xXsn2U7jt/Flv0oa97XGvAjtOtxPawwN227d3zlnL3trvEFCDJhfv3sx96EbBkaAClLsRe1ViDzUYlChjyyr3y+EvgL9jgZ1vsj09TkrccArwjtnLg9cAvjTEhYCb2itEn2Ipz+aisdi8wD1vkYAnwpwTtb3HbtAe2H/xX2H03EFrnF1nV2YYYW378ROBW7AH2IOBwY6se4bbt9243gYkZrO9L7Jil/8Pu9/OBw2KCl0wl3EfGmCewB6OHsVeGR2C7LkQ9jw10n3Mfv4698hd9jDFmGXAkti/6l9hyrReS4rdKRCZL+jkxTsQeJNdmsoFKqfxy/2+/iB0P+Wjcy78B/uD+xl+BPZHNxG3Ak8BrwCLgoZjPWwP81l3XN9ig7NGY1/+H/S1b6v6GtrtIY4xZgi3w8Ffsb/zPsVODhDJsW6zdgZfEVpV7FDsO60O37Y9jiwN9hB0bm2v3rqi7sONsPsMWKPhtkuVqsEHm29j98yC2OAIiMkJsZbxsq5y2Y4xpxFZbvBB7zLkEe8yJZsRuBI4WW9H1pgzWF8Ieww7Gfid/A2rc7zIbCfdRuu/cGPMudgxag/v4W2AptiBS2H1uDTZQPxZ7bvIZcA02mE9IRH4tIulKtZ8ABI0xH2S5rapARDOBqtjElk79BDjaGNOQbvkCtmMldlBwoFhtKHW6j5RSqjSJSABbBOP2YrelVOk+UvmiGShVFCJykNh5KCqw2YYWbEUbpZRSSimlSpYGUKpYfoJNfX+F7Zo2zhiTrAufUkoppZRSJUG78CmllFJKKaVUhjQDpZRSSimllFIZ0gBKKaWUUkoppTJUVoiVDh482IwcObIQq1ZKKdXNLVy48CtjTNK537qKHquUUkqlkux4VZAAauTIkSxYsKAQq1ZKKdXNichHxW4D6LFKKaVUasmOV9qFTymllFJKKaUypAGUUkoppZRSSmVIAyillOqFgkGorbX/KqWUUj1F8N16ahtqCa4o3AEuozFQInI+cBpggDeAk40xGwrWKqWUUgUTDEJ1NYRC4PNBfT04TrFbpZRS+RFcESSwLIB/pB9nuP649Xjr10NDA8ybR3DRv6je611CPi8+r4/6mvqC/A2kDaBEZBjwW2BHY8x6EbkfOBb4R95bo5RSquACARs8hcP230BAAyilVM8QXBGkuq6aUDhU0BNoVWSrVsF//gMPPwyPPw7r1kF5OYFjtyJULoRNmFA4RGBZoCDff6Zd+MqAviJSBvQDPsl7S5RSSnUJv99mnrxe+6/fX+wWKaVUfgSWBQiFQ+1OoFUP8dlnMH06HHQQDBkCxx8PL7wANTUwdy588w3+q+/GV9YHr9gMlH+kvyBNSZuBMsZ8LCLXA8uB9cBTxpin4pcTkQnABIARI0bku51KKaXyxHFst71AwAZPmn1SSvUU/pF+fF5fawaqUCfQqossWwZz5thM04svgjGw7bYwcSIceSTsuSd42vJBzkYO9TX1Be/CKcaY1AuIbALMAX4JrAIeAB40xsxO9p6qqiqjc2sopZRKREQWGmOqit0OPVYp1TPpGKhu7ptv4IEH4K674Pnn7XM772wDpqOOgp12AhGg8N91suNVJkUkxgIfGmO+dFf0ELA3kDSAUkoppZRSqhic4U6vDpwyDSpKKtAMheCJJ2zQ9Oij9vEPfgBXXw3HHgujRrW1+fmprZnFYo13yySAWg7sJSL9sF34qgG9ZKeUUkoppVQJiS+iMe2gaTSua+wQJKUqttFlgZUx8PLLcNddBAN3EdjkW/yrBuGceSaccALstltrpilRm0/c+cQO491KJoAyxrwkIg8Ci4AW4FVgRqEbppRSSimllMpcbBGNppYmzpl7DhET6RAkJSq24Qx3uqaK4bJlMHu2zTa9+y7BUT6qjw8T8njwlTVRX3MszvCOvbzj2wwUbbxbRvNAGWOuBK4scFuUUkoppZRSOYotoiFiy3lHTKRDhiZZsY1kgVWnJRrX9NOfwiWXEBixnFDwasIJ2hmbDYtvc83ONdTsXFOUbogZBVBKKaWUUkqp0uYMb6tCV9mvkolPTCQUDuH1eFm+ejnBFcHWMWKJqtXltYphU5MtLz57Njz2mB3XtMMOdlzTr38NW29tP3NFEN/L13X4zETZsERtLsb4rbRV+HKhlY2UUqo4gsH8lSfP57piaRU+pZTqGsEVQepeq2Pm4pm0RFoy6pbXqTFQxthy43fdBfffbzNPm28Oxx1nxzXtsku7cU2pPrO2oZbJ8ycTNmG84uWq/a5i0phJ2bWnkzpThU8ppVQ3EAxCdbW9yOfz2bmecg188rkupZRSxeEMdwgsC9ASacm4W15OVQzffddmmmbPhg8/hH79bNnx44+HsWOhLHXIkegzS3lOLw2glFKqhwgEbMATDtt/A4Hcg54O65pvcIYstaVkE1w9VEopVZoKFoh89hk8+KDNNr38sp3Qtroafv97GDcOBgxIu4pU2a5k3QxLgQZQSinVQ/j9NlsUzRr5/Z1Y15gwvjIIRcBnWvBfdyRc9ritnuT2W1dKKVX68hqIfPghPPwwPPSQ7apnDPz4x3D99bab3pZbZryqTCr+leqcXhpAKdXLFGpciyo+x7Fd7ZJ9v2m/+5Ur4emn4ckncebNo77pewTw4x/2AYx2qK2Ygv9/m+Bo/KSUUkWV7TilnAMRY+Cdd2zA9NBD8Oqr9vkf/9hmmo46Cn74w+zXS24V/0pl8l8NoJTqRXRcS8/nOIm/04Tf/c7roKEBnnwSnnoK3nrLLjx0KBx2GM7I7j9nAAAgAElEQVQBB+BUVxNcunnbe58s3b8bERkE3A7sBBjgFGNMsLitUqr7KpWT1Xil2q5k8tXe6Hpiq+sVZK4mY2DBAnjoIYLP3U3AswL/MnBG7G0zTUceabtzd1K2XQu7ZI6qDGkApVQvks8xMqp7affdN0UInDgLZ/lZtsxsRQWMGQMnnQQHHgg77QQiNmN1Jyxf3m3+bm4EnjDGHC0iPqBfsRukVHdVSierpdiuTIOifLU3dj0iQsREEs7vlLNvv7VXx+bOhccfh48/JjjCw34nGUIe8Hl8zD/p+oSfk2uAmG3XwoLNUZUDDaCU6kXyOUamNyu1bpBp2/Puu/hXvozPHE0IL75IM/51c+Gss2zAtO++tmJS3DqjWSevt62AUqn+3YjIQGBf4CQAY0wICBWzTUp1Z6V0slpq7comKMpXe2PX4zEevB4vguReFGLtWnjhBZg/394WLLBXyQYOJHj4LgSqHF7edB1NS+cC0BQJUfdaXYe2Z7Iv0hWKyHR/lFJVPg2glOpF0o2RUemVWjfIhO3Zy8DChfDII3aw79tv4wD1288jMKIG/4lb4/z6/pTV9GIzVgCnnw4jRpT0380o4EtgpojsDCwEzjPGrI1dSEQmABMARowY0eWNVKq7KKWT1Vil0K5sgqJ8tTd+PdMOmkbjusbMsz5r19pKedGA6aWXoLnZXh3bYw+49FL42c8IjvBQfc+BhFaHYHX61cbui6aWJqYEpjDFP6W1TfnMGJZSVT4NoJTqZZKNkemtss0mFaobZK5ZrQ5d8y54DGfl2bYghNdrs0tnnAHjxuGMGEGmq47PVtbUlPzfTRmwK3CuMeYlEbkRuBSYHLuQMWYGMAPsRLpd3kqlCixf421K4WQ10baUQruyCYry1d749YANXhJqboa337YFH155xR5gXn/dHig8HqiqggsugP32g332gf79W98aaKhty3ThwSteIiZCubecmp1rku6LppYmIkSY9+E8GpY3tAZK+c4YlkpVPg2glFK9Vi7ZpM50g0wWJMW3Y9o0aGzMIJhauxa/LMDHXoTw4I2EWf7KZwT3/hXOH3eEww6DysrMGxijG2YrVwIrjTEvuY8fxAZQSvUa+R4fVMyT1VTbUuyT6GyDony1N7qedvvGU0796OtwVooNmF59Fd580x5QwM7FtOeeMGmS/SHfZx/YeOPWdQZXBAm82rYd2Wa6ovtiSmAK8z6c12FcVilkDAtBAyilOqmY42FKbSxOd5NLNik+sACorU3/HaQK1mLb0dQE55wDkUiSoK6xEf79b9s976mncNavp37AAdQNvYSZH/q5LXI6sxYI9deAk1vs1C0ZYz4TkRUi8n1jzBKgGni72O1SqiuVwvigfCn1bemyIK6lxU5Y+/HH8P778O67BL56mNDg9YQ9EAqFCVx/Ls7zwKabwq67wnnnwS672PvbbWd7IySQLEjNNmPmDHeY4p9Cw/KGDoFSKWQMC0EDKKU6oTPjYTob/MyYYU+0w2FbRK3YY3G6o1yzSdFukNl8/6mCtdh2iNjjpTE2mAoEwNnyIxswPfIIPPecja6GD4fTToMjj8QZM4bAdWW0TM5f18JSG+uVoXOBu90KfEuBk4vcHqU6KGQJ7i6/2m+Mrd721Vf21thob19/DWvWwHffdbytXWt/5Fpa7A9W7L/R+yL4t2jGNzZiK8BFIvj/MAu+fcSO2YnefD7o27f9rU+fjs8ler6iwj6X6FZWgNPjcBjWr7e3deva/l21qm2/NTbCJ5/YYCl6+/xz+5sf5fHgr9oC34EeQsbg85bhP/96uOdI2GqrlGNb4yULUnMJDlMFSsXOGBaCBlBKdUKu42E6e3IaDMLZZ9tjDcScaPes36eC62w3tUy+/xkzYM4cO+dgsmAtth2rVsG119qhOZEIVN76R/jdFXbBnXaC3/0Oxo2zVxZjDpT5rrDYHUveG2MWA1XFbodS8bpq/p68X+0Ph2HFCpv5eO89+PDDthP76In+unXJ3+/z2fE1AwbYf/v3txU/+/ZtC4KiZT6j991sidPSQv2Kzwj0/Rz/6k1xKgbCJjFBVlOTDdKiQUnsbcOG1iYEt4LASOw8Risz3G6vt2OAVVFhxw+JpL9t2NAxUIp2qUtnk01g2DB7+9GP2u4PG2bnXtp2W5yKCupzCMTjg/d8B9w9MVBKRgMopToh15PW+C5bU6bYW6YnqIFA+wtSXm9+xuIU6n1dIde2daaoRrrvf8YMW78B7Dy1l1wCgwYlbqPzg29wVs6j9loPHo4gQhkeWmj0bAbXXQdHHAHf+17K7cjnmCUtea9UfhR8/p44OZ3Erlljiwy88YYNlKK3pUvbn/j7fPZEfsstbRexQw+194cMgcGD7ZjLwYNtV7L+/e3yndkW95Y1N4UfXPos1Q+NawtY97gZp8/37IF3w4b2t0TPxT8fidh1p7tFs139+rUFjMn+HTTI7rfKSth0U4JfLMooMEr2PSfLcOaru56yNIBSqhPSnbQmO6mPnpw2Ndnf43nzoKEh80yU328vhjU12QtiN9/c9r50gUSu2a907yv2WLBidDdL9/3PmdP+8eLF8OST7oOWFlsd6ckn7e3llyESwb/RWCq8hxEyHnw+L/57z8z4DCKfFRa7YREJpUpS3ufv6ayvvrIlrBcsgNdes7elS9te79vXjpvZYQc4/HB7f7vt7AWcLbe0B51SJwJ9+hBoXEQo0kyYCKFIM4G+X+CMObXYrUsq1yIgmWQ489ldT2UYQInIIOB2YCfAAKcYY4KFbJhS3UWyk9ZUJ/XRk9MpU2zwFIlk100q2cltJoFEINAWuG3YAHV1nS/fXezxMnV1dluMSb8fYwM96HyAEFsIIvYxwPjxNvPU+ni/Rrj9YRswzZtn++t5PLD77nD55XDggTh77EH9K2Vpg/LKygwr9XWClrxXqvM6PX9PZ4TDNrP0/PM2aPrvf+GDD+xrIrD99rDbbnDKKbDzzrbL2FZb5T1IKuS4r1S6WwW4XApnZJrh7G77otRlmoG6EXjCGHO0Ozi3X7o3KNXTpcu4pBtD4jg2gGpo6Fw3qTfeaGtHJuNWKivbuv8ZY7uZ7bILTJiQehtTdekq9HiZVPs6GIQ777TbArYbvd+f+D2xgZ7X21awoTNBX9LgMRJhwpglcEIjc+o3ZvyGu5kw6Rr7pmHD4Kij4MADYexY290lRrqgPBoAezxaQESpUpeum1Reg4tIxGaUAgF7e+45e6EGbPZor73sj/1ee9nAaaONOvd5Gch3afVkn5GseEEuXdS6U8CXaYZTu+vlV9oASkQGAvsCJwEYY0JAhiPhlOoZ4k/GM8m4pAo4YteXSzepZCfS06alH7fS2GgDh2jAEYnYan6jR3fskhe/jcnams14mUT7MpMuh9Huirfc0j7YCwRs4AZ2u052664l+n5iA73YILIzQV+74LEpQmDyfJyy6wi+ECHw3W74CTCh8j2Co0+ndsAL+I/dAudX22RVKSn+s6JtzzZzqZQqjlTjVTodXHz1lU11P/44PPGEfQy2293RR9sf1333tZU7iyA+q1L3Wl1eT+LT7cNsu6hl853kO9DKJcjJJsOp3fXyJ5MM1CjgS2CmiOwMLATOM8asjV1IRCYAEwBGjBiR73YqVTSJTuAbG9NnXLLpZjdpUnZtSnYi3djY8TPjAxS/H8rL248LDoc7bkOirFJ0Hr54mY6XSTRh7MSJqcdVTZnS1j0vUbAXH7zV1HRse11dW7e36LLRDFRzs/03q/lmP//cXuVdvBj//LX4IpcSogxfpBl//WSCo0ZRvf5OQlKGrwKmXQ0Tz/fYNtZD/ajkwWKqfRg/ds7j0QIPSnVnOc11ZIz9/Xn0URs0vfSSfW7wYDjoIJvZ9vttV7wSEHuC7/V4mbl4Ji2Rlrxlo/I9X1Sm6ytUZi3bIEczS8WRSQBVBuwKnGuMeUlEbsTO7j45diFjzAxgBkBVVZXJd0OVKpbYMUPRE/ibb84s45JofEzs+nItPx5/Ih2tnFpZ2b77V7JMWSAA115r52M1xmavKivbTwibbRW2TMbLxAc2c+akH1fV1NSWLYOOwV6y4C02UJo5s62r3rRpbWOH3nijbS6tiRPjsnDhMMFHPifwn7X4N3kNp/k5WLLEjif47DOC7EUAP/4h71C/92QCfQ/Bf/gAnBOfoPaWgYQmQ9hAqBnmPJQ+4M4kqxm7rV0xBkopVViZdtkKLn+RwH/vxf/aapz7g7asuIgdP3nllXDwwbZLXpIJUwstVSYm9gR/+erl3LbotrxOjpvvsT2Zrq+UJvrVzFLXyySAWgmsNMa85D5+EBtAKdUppVYSO1XFPI+nLdsTDifO9CRbZ/xJcewYpEgkeeYj1f6Jnzfo+uttgBCfnYkNWOLLpT/8cPuCBIkyQfmowpZqHNX48YnHgMVnnqBtTHNFRcdgLj54i2378uVw221twUtjo5vxa2kh8Mh3RMIbE4kIoQ0RAhc/jrPZ7fDuuwTfraS65QlCDMHHMOr7/R3nB9/AgQcSHHQw1X8/mlCLB98aof46mBQT+C1f3jYPY6rtjJXpODIt7KBU6cu0a1fK7IEx8OqrBB+cRrV3NiGPwSdQP7oK5+Lpdj64IUO6YGtSyyQTEz3BD64IMuu1WXktZJDvDEym69OiDL1b2gDKGPOZiKwQke8bY5YA1cDbhW+a6smKVbUtWVCSrj2HHdY+WxN9f7YZl2gmKhqQicCrr8a9KRQiOPcbqo8dTKhZ8JVFqL/wcZzN3ofVq+3t229xVq/GWbeOI1+ZRCTyE0BobjZce8A8Ht7sDGhuxt+0K77IP2minEjEy7ynIjQ8HaJ+81/j9HkVRwRHhNo1ZxNa/1vClBFa30Lg+Jk435uD4/Ph+Hzwhg+m++zG+3xtkwpGJxpMcj/4/mb4L9yF5hYP5eWGwF0fU3+Pj8ArG9l9uG85o3fyEXhWOowviw2eAC66CAZtbPDv04yzwzr4dH3bZIXr2+4HX+1D3dNDoaWFmtGvwvrVzOJkQnjxmWb8t58Gf30GPv8cf2QPfNQToty+tuB6GPU5bL89gcozCD3fh7DxEPJ6CVz2NM7v7LilurNgQ6jj+Kn4IhWnn267FDqODWwz6Z6n8y4p1b1l27WrQ/Zg6VL4xz/gnnvggw8I7CuE9jOEBftbdP5ROGMmJF1fPrcjk6Akm0xMobqb5TsDk8n6tOtc75ZpFb5zgbvdCnxLgZML1yTVG2R6tT2fUgVJydoTf0J8yiltJ8SZ6HBSvNcG+PRTyjwjCEU8GAMzZ7RQs+BCnA3z4dNPobGRAJcS4irCeAmFwgRqX8Rhql1p//6w8cYwcCBstBGfbGhfwe2T8pGwzz5QXo4jwrT/3cdVi3/OynWbEsFLCB+Bocfh7NTfviESwf9pI75AmFAEfJ4W/AMW2dRWKGRTV6FQ+/vRyQUjkbaubARw+G+7ttTxN0LsBogdi/TLx7iV39hpjf5kl3HcW7QfYsBcSsj8HkMZdtYEwUMLg/78eyZFrm4fVcV/x+yFn/mEqABg5vytme8ZS/3AfxEo/xn+oUtwRq2FykNg2DCcrbaifs1rBD7aBv8h/XAOeKa1uIM/CD63C6GIUDm47e8oUdW/+L8jgBEj2nc1TPV34zi2e+GcOTZjpVkmpbqnnLp2rV8PDz1kf1yecX+Hxo6FSy/Fv9cwfP8a36WZjmyCwGwzMbkUdSjVIEW7zvVeGQVQxpjFQFWB26J6kWJcbU8VtCVrT6oT4qQiEfjwQ3jzTZw336T+p2EC7wzB/91/cPZ/DIBT+BvTmYDBS0tECHy+A85uK2DMGNhiC/zrdsZ3gxBqieD1eln+i0kET5iEM3ajDn3cT50BL58RfSScOvV7MOEuwJ7sn+tvXzCizOfFf8sx4BzT+pwD1Ldm58pwnFsz2qfBhhaqD/Ta/VZuqL/rU5wffts2c3vtCHg05g37V8Nxt9nXo8tEBzm5N//K4fjujtDUEiaCB49EqPBG8P9icxh1mZ3kMXqLzvbu3gIPbkfzbRVgbBAUkgoCVz3HpN95ks5F2xrAJXDggTbzGIm0jZEKBDpW/Uv3d5TRvgy2daNsaOhYFVEp1T1kHFAYA4sW2aDpnnvsRatttoGrroITT2ytmucA9Rt3baajFLJK0DUl0JXKRaYZKKXyKl/ja7KR6uQ2WXvSnhB/842dzf311+HNN+3t7bdh3bq2dQ8fjrPttjBqFIz6I4waRc3aHzHrXA+hZoPPV0bl5WdR23hWW9dAoP4IWz1u5ky47Z8DmfVQ4q6O0ZLe0czF6NFtxSACAVtlLlbsCX+sdBmSROXHp/yxrLWQRQgh8N4wnKOHtb6n5lKY+WR0/wk1f9wenO2Tfwjutp8RWyhB8Pt9OM45Kd8H4O8P5f9oCxh9PsG/X/blwhN1I4wG3Ymq/rW2vRN/18XIyiql8i9tQPHNNzB7Ntxxh62m16eP/fE+9VT46U8TTmKbTaYjHxmbQmeVMlVKhRqUiiUmRXeYXFVVVZkFCxbkfb2q5+qqghK5fE7re5wmnPIFBB9YaR9/9SDOxw+2LTh0KOy0U/vbjjvaLncJ1hetopasgAPYQGjyZHtS7fXaC5OpSp4nKhN+7rltAUVFBcyfn/0+TlZ+PJMJXbu6WEgwaANPyK67ZfS98YUnwGaa+vRp275CbFOxxgUWg4gsNMYUvVeDHqtUl3rrLbjpJrjrLttlb7fdbNB03HEwaFBePiKfGZtS6DqnGShVbMmOV5qBUkXXmRPHbE9ks6pgtmYNvPACznPP4Tz7LFz5CsHm3aimnhA+fN5x1J/5c5tx+fGPM5pIKNG25tK1MJn4dTU22udyDSiSrTdafjwaPI0d21bdL15XV43L9fPix7tFq+glGvtWiG0qRlZWKZVfHYKOcBj+8x8bONXX2ysxxx8PZ59tjxt5ls+MTSmM79FCDapUaQClii7Xrkv5uGLfLgDbtQmee87O6P7ss7ZvejT1U1UF559P4JMTCd3bl3BYCFFGYEQNTnVu2xotKz5+fPZdC5NJFHBlc7KfLHuTrvx4suCpK3U2KxQ/3u300+2Yt64MZrREuVLdV4dsie90nL8/ZqvqDR8OU6fCaadlOWt3dnpiae1SCOSUiqcBlCq6XAfed3bMSDAI1ftHCDWBT5qpLz8IpylgG7HHHnDppbY/uuO0dsPzB8E3J/fiF/ET4M6bZwOR2MldE835lOl2dSaLEQza90S7+82c2dbdL9F605Xl7kr5CKYTjW0q9nYppbqPdtmf0HoCT9yEM2wMXHONnbOprPCnXJqxUapraAClii7Xk/6cAq9oibPHHydQN4zQhnPt3EfGS+DHE3EuvxD22w822iivbY1//5QpNniKROImd82DXLMY8QUn4oPSRJPVlkqAkSqYzjQzpV3olFI5e/99/Pe/hG/jMCEP+MSL/8rbYGzXz/qiGRulCk8DKFUScjkZz/iEt6kJnn4aHngA/vUvOxGtz4f/R2fgW2UIRQw+nxf/DUckr2fdybbGv3/KlPZd4PJdxj2X7mx+P5SXx1aw6z6TucZm9kTaeshkm5kqpaBQKdU5XVIE4dVXbYbpgQdwysqon3AEgert8O82XoMYpXowDaBUQWV7Ip+36maJgqZBg+DII+1t//1x+vePmfuo6wsdZJrtyGUfpgsaEq3TcfJTcKIYopPQnnOOzULFztmkpcGV6n2yrd6WdbDV0ABXXw1PPgkDBsDFF8N55+EMHdruOlyy9ZZChTulVO40gFIFk+3V/+gYnOZmmwlJd7LbYf1PNOOseQruv78taNp4YxswHXOMLRXn87VbR3zGIVkRhUKUrc4k25HL2J50QUOqdXbnDExjo+0SGe0WmWjOpmhGratLqyululY21eiyCraef952IaivhyFD4E9/grPOSliGPNl6tTS3Ut2fBlCqYLK9+l9X19Z9LBSyj1Mtb9dvbEW8DWECB9bibLjSBk3jxsEvfpEwaEomWREFKN78PLlkUNKNDeupWZlkFQjjM329ab4lpXqrbKrRZRRsvfgiXHmlHbw6ZAj8+c9w5pnQr1/W69XJYZXq/jSAUgWTa3W9jHz6Kf7lT+OLHEOIMnymGf+YMPz23/Czn9lZXbOUrIhC9H5XBhyxk+1G96HXayd4DQY7VwyhoN9LESXb7visWk8NIJUlIl5gAfCxMeawYrdHFUc21ehSBlvBoA2cnn7aBk7XX28zTikCp3Tr7YmlxpXqbcQYk/eV6uzuKiqbrlLBoC2AFz2xj5bQbtXcDHPnwh13wNy5BMO7U7fF/8EPfkDNZcNxxiaunJdpO+vq7KqjQVRFRXEyUPEZkmnT7DjlmTOhpSU/bejNXdg0A1V8yWZ2z9O6LwCqgIHpAig9VqmoDmOSFi6Eyy6zY5w22wwuucQGTkkqtGa83jTPK6VKS7LjlWagVEFlO4fR/PkJTuzfew9uvx1mzYLPP4cttiD4q79S/cAZhL704FsNNTnGTtHAKRqceL22998WW7QfA9WV5a3jMySNjXZC15aW/GVNuvNYp87ScuU9l4hsBRwKXA1cUOTmqG6ktfT3kiW2+/cDD9guANdeC7/5TdaBU4f1Zvi8Uqp70ABKlZToiX3w+TC1Ne/iXzId5+UbbWRz2GFw6qlw8MEErisj1Ny5gCKaidiwAWITsXvs0XFOpq4MOJJ1sct3t7venIXqzQFkDzcNuAQYkGwBEZkATAAYMWJEFzVLlbyVKwlOPZvAG//G/2kFzhVXwIUXwsCBxW6ZUqoEaQClulTak/bGRoJXPE71reMJme/hk1qm/byaxp1+iv/nA1vfk49xPNFMTzR4EimNMUHJMiT5zJpoNzbV04jIYcAXxpiFIuJPtpwxZgYwA2wXvi5qnipVX38NU6cSnHMj1ceFCO0n+MqhvuYgHA2elFJJaAClukzKk/ZFi+Dmm+HeewlsmEiI4wjjpUm8nPPEz4nMBd+0tvfkoxtWbBDm9cIpp5TO3EeJMiT5zJpoIQXVA+0DHC4ihwB9gIEiMtsYc3yR26VK0dq1cOONtovet98SOHcXQuWLCRMhFG7WynhKqZQ8xW6A6p6CQaittf8mepxIh5P2+jDcfbc9c99tNzt/00kn4b/rNHx9vXi94PEI4XD7E/0ox7Fd7WLnakrXhthlokHYVVfZ9d56q10m3Tqy2S+lKho8er2lkXVTqrOMMZOMMVsZY0YCxwLPaPDU8wRXBKltqCW4Iscf2ZYWmD4dtt3WFonw++H11/FfdDO+sgq84tXKeEqptDLOQGlpWBUVn0k66ii4917bFa5Pn/aZpdhy3MuXQ1kZgMHnacF/43j46t+w/fb2SuCJJ8LGG+MA9du2vW/ixPRd9TLpkpZsmdi2dqZbW3fqFqeFFJRS3U2nJqA1Bp54Ai66CN5+G37yE3joIdh7bwB73Mmw7HkhaXU+pbqHbLrwnQe8A2in4F4uNpO0YYNNIkU1NbV1B4sGFE1NEImAx2MokxZOl1nUNN+B86N+cOF/4KCDwNM+GRob2Iwenf5EP5MuaemW6Wy3tu7WLU4LKaieyhgTAAJFbobKs5wnoH3tNRs4zZsH221nA6dx4+zA1xjFrozXqQBRKdWlMurCF1Ma9vbCNkd1B7Hdv+KOP62vQ1tAEYkAGCIR2x1vxA8H4Cz6m02BHHJIu+ApURe42K56ybrIZdIlLd0yne3Wpt3ier7u0kVTqZ4oOgFtxt3sPvnEDm7dZRc7znbaNHjrLTjyyMQHryJLFCAqpUpTphkoLQ2rWsV2/1q1yo7BjbroIjerEYngL38Rn6miiTIiePFIBF+FF/+tv4RdOq43XRe4VK9n0iUt3TKd7dam3eJ6tu7URVOpnsgZ7mTWzW7tWrjuOntrboYLLrDjnTbZpGsbnKVogBjNQOk4LKVKV9oASkvDqkSiJ46BgJ2gffFiGD8eJpwahn8+AH/6E84bb1A/5AgCVRdSeWAVjWv7dqobXrrXM+mSlm6ZznZr025xPVd366KpVE+UsptdOGwnXL/8cvj0UzjmGJg6FUaNKlh78jlmKeMAUSlVdJlkoLQ0rOogGLRZlmgJ8L/d1MKEsjrYYSq89x7ssAPU1eEceyxOeXlG60w3t1M+5n5SKlf696dU5xWsSMLTT9suEK+/DnvtBQ8+2FogolAKMWap2OOwlFKZSRtAGWMmAZMA3AzURRo8FU/aiWi7SF2dPZEECIcNvzkbRnMbzi797YHryCM7FIZIp9Bd7JTqDP37U6pzClIk4a234OKL4fHHYZtt4L77bOYpyRinfAZwORe1UEp1ezqRbjdSUmMwQiGgHBBACONhyq6PMuWvg3H2ltb2ZnuyWegudkp1hv79KZW7vAYcn38OV1wBt98OAwYQvOYcAnsMwb/tcJwUwVM+Azgds6RU75VVAKWlYYurJMZgfP013HQTNQ8EuYNHacbnvuBh3uLNaBhrAzvILNgrlYyaUkqpwspLwLF+Pdxwgy2HuWEDnHMOwdMPpvpfRxF6LoTvhdqkgVG+M0Y6Zkmp3kszUN1IUcdgfPYZ/OUvcOut8N13OEccwc07fsJvrt2GcNguEom0BXaQPtgrqYyaUkqpgupUwGEM/POfcOmldlb2cePgmmtg++0JNNTSFG4iYiI0hZuSBkaFyBjpmCWleicNoLqRoozB+OgjWwr29tttOdhf/tJOyjR6NI217RcVaR/YpQv2SiKjppRSqsvkFHAEg3D++fDSS3ZOp1mz2h1UKvtVEjERACImQmW/yqSfHRvAAdQ21Gr2SCmVNQ2gupl8jsGI7z7X7nHlu7b861132ciopsZe+dtuu9b3x2bEvF47J+4WW7S1M12wp1XNlFJKJbVsmT3u3HcfDB0KM2fCCSfYA06MxnWNePAQIYIHD43rGpOuMhrAFaSghVKq19AAqodKN7YovvvctGkwcSKEmgw+CVEfOeW8UXAAACAASURBVBmnYhGcdZatcDR8eId1xAZJlZXu+0P24mC0O166ghBa1UwppVQ7335rxzjdcIOt5nrFFfY41L9/wsX9I/1UlFVk1TVPK+gppTpDA6geKJOxRfHd5+bcuZrQ+v6E8RLCQ2DfyTj37wKbb57ys6JBUm1t9t3xtICEUkqpVi0tcMcdMHkyfPmlzTb96U+w1VYp35bL2CqtoKeU6gwNoHqgTMYWtXafa4rgMyHGv3QRDdxISCrw9SnDP/UgSB07JV5fht3xtICEUkqpVk89BRdeCG++CWPGwNy5UFWVdPH4+ZyyHVulFfSUUp2hAVQPlEkw44SepX7HBwks7I9/48U4/7cvo3c3BF7x5pQRyrY7nhaQUEopxdtvw0UX2YlwR42COXPsROxJ5nKC/M3npBX0lFK50gCqG0vWBS5pMGMMzJsHV10FDQ04m2+Oc/3FcOaDsNFGOIAzNvf2ZFPgQgtIKKVUL/bllzBlCkyfbsc2XX89nHMOVFSkfauOX1JKFZsGUN1Uui5w7YIZY2x3iKuusmVghw2Dm26C006Dvn2L0n4tIKGUUr1QUxP89a/wxz/Cd9/ZQkVXXgmDB2e8Ch2/pJQqNg2guqmMusBFIvCvf9kD1aJFsPXWdiLck08muKiCwLTiBi/5LMmulFKqhBlju+ddcgl8+CEceqidY3CHHbJaTXTs07SDptG4rlHHLymlikIDqG4qZRe4cNgeqP74R3jjDdh2W1vZ6IQToLxcCzgopZTKq/iiDu288ootENHQAKNH24IRP/tZTp+hczcppUqBBlAlItF4plRlvhN2gWtpIXjVPAK3vo3/ywdwftBsJ8I99lgoa/uqtYCDUkqpfEka2HzwAVx2mZ0Id8gQmDEDTjmlw0S40XWkq4inY5+UUqVCA6gSkCgjBOmzRK1d4Jqb4c67CF7xONUfzyLEWHy+31J/m+D8pOOBSgs4KKWUypcOgc3b/8H5833wt79BebmdCPeii2DAgITvzzSzpGOflFKlQgOoElBXBxs22C7i0YwQJM4SzZhhe+eNHw8TTmyCmTNh6lT46CMCW/6VkKcP4YiHUBgCDeD8pOPnaQEHpZRS+eIf6afMU0YkHKHMQOUfrqfWG8I/4XCcy26FoUNTvj/TzJLO3aSUKhUaQBVZMAh33mmDJ7A97aIZofgs0YwZcMYZAIanngIuuZwJq6+HPfeEW27Bv8kh+MZKysxSbLfASZMKv31KKdUVRGQ4UAdsAUSAGcaYG4vbql4iEsaEwxAxhCNhfjvW0OIRfGVPUd+yDIfUAVQ2mSWdu0kpVQo0gCqyQMBmmcDOG3jyyW0Zofgs0ZRJG4AKQADDHO8vmPDUATB2LIjgJHhPLC0eoZTqwVqAC40xi0RkALBQRJ42xrxd7Ib1WMbAk08SuOMkwju2YDwQ9ghhDAaT8TglzSwppbobDaCKLH48Uk1NgizRO+/AqdczvqGcp7gVsOmq8bW7Q1who1SlwbV4hFKqpzLGfAp86t5fIyLvAMMADaAKYeFCuPRSmDcP/x5b4hvtI0QYr8eLILREWrIap6SZJaVUd5I2gNJuEYUVPx4JYrJEZWHqd5+E8/x10LcvE846BYZ+xZznNrNjoCZk91laPEIp1RuIyEhgF+ClBK9NACYAjBgxokvb1SO88w5MnmwH41ZWwo034px5JvWfL2zNIAGaTVJK9WhiooNvki0gMhQYGtstAhiXqltEVVWVWbBgQX5b2oVSlQ8vtLPOjDB9umAQvDRzVd+pTLokDGefDZtt1un1F3PblFIKQEQWGmOqCrTu/sCzwNXGmIdSLdvdj1Vd6qOP4Pe/h1mzoF8/O6/TBRfAwIHFbplSShVMsuNV2gxUb+sW0dXjhFoDmr1D8OST3Dn9AAw+wFBWJvgfuwj275u3z0vVxU8ppbozESkH5gB3pwueVIa++AKuvhr+/nc7UPe882zf8jxc0FNKqe4qqzFQvaFbRFeOE7LBmiG0weAzYU5kJWG8gCBiOPm0Mpz9dZiaUkqlIyIC3AG8Y4z5S7Hb0+2tXg3XXQfTptl5Nk4+2c7nNHx4sVumlFJF58l0QbdbxBxgojHm2/jXjTEzjDFVxpiqzbrxlanoOCGvt8DjhD7+mMD/PU5ofZiw8RCiHA49DF9fL14v9Okj1NQU6LOVUqrn2Qc4AdhfRBa7t0OK3ahuZ906uPZa2GYbm3k67DB4+2247TYNnpRSypVReqM3dYso+CSzb78N118Ps2fjb9kdn3d/Qnjx+cqouWw4NZfpGCWllMqWMeZ57BwPKhfNzXD77QRnTCbQvxH/2L1wJv0Ndtml2C1TSqmSk0kVvl7XLSKf44SCQQjMN/g3fR1n7mT497+hb1844wycCy6g/rOKDgGTBk5KKaW6RCQC994LV1xBMLSU6pM9hLwefGWvUT94A3o4UkqpjjLJQEW7RbwhIovd535njJlbuGb1DMEXIlTvHyEUAi/f55SKI6k59QicqUfA4MEAONtowKSUUqqLGWMv6F1+ObzxBuy8M4FzTiT0yWzCJpzxJLhKKdUbpR0DZYx53hgjxpgfGWN+7N40eEplwwa4/XYC426wBSkoI0QFf286Cf9dpxJ8b3CxW6iUUqq3CgRgn33giCNg/XqbgVq0CP+BZ+Dz+vCKN6tJcJVSqrfpVSXeOjsHUuz7IcG6Vq2ypV5vvBE++wz/9ifi+xbWhwzRrvmhENTVadZJKaVUF1uwAC67DJ56CoYNgxkz4KSToLwcAGe4Q31NvU6Cq5RSafSYACpdcNTZ+Z1i3+/12ukwWlrcdd37BU7DtTB9Onz3HRxwAMyejbP//tT/V5g4EV5+OV9bqpRSSmXhf/+Dyy8n+NIcAjv2w3/NOTjnXmvH48ZxhjsaOCmlVBo9IoDKJDjKdn6n+IAs9v2RiF3GGAhtCBM46kYcmQa/+AVcfHG7qkWOY6fR2G+/tvbFlyfvbGZMKaWU6uCjj+D3v4dZswhuW0H1qWWEPE34mu+g/qtfaaCklFI56hEBVCbBUXR+p2gQk2p+p0QBWdv7DV5PBAmHaTGCzzTjH18J175n581IwHFg/vz2QVI0aKqshIkT2z5r2jRobMxPMKWBmVJK9UJffGHncPr73213ifPOI3BAP0IvT9UCEUoplQc9IoDKJDjKZn6nRAGZf0yYE8csg8WvUfPFdbDxIAJVF+G/cDecgy9I28bY0uixAZqIzWhFItDUBOecY+/n0s0wVme7LCqllOpmVq+28wzecIMtZnTyyXDFFTB8OP4VQXwL/0IoHNICEUop1Uk9IoCKdpObMwfGj08eKCSa3ylRlqayEjwe20XP5zNUvvUc1ZftQchsjU+2pOZCg/OHg3H69cupvbEBmsfTNqZKpK2LYCbdDDP9jM6uSymlVAlbvx5uvhmmToWvv7bdyf/wB/j+91sX0QIRSimVPz0igAoG27rBNTTA6NGZBQuJsjRg1xUOGzxEmFZ2CY13+wixjy1H7vESqByPk0PsFNttLzZjFu22F9+dL1U3w3Sy6bKolFKqGwqHYeZMuPJK+OQTOOgg23Vv110TLq4FIpRSKj96RACVa7Yl0ftY+x2hDX2JGC9ChMbNf4j/6h/h+z+vG4xITsFIfLCWbKzT6NH5GbeUTZdFpZRS3Ygx8OSTtmjRm2/aH/h774V99y12y5RSqlfoEQFUomxLJgUU2r2vPIJ/8U3wyCP4zFxCVOCr8OKvO8UGI1WdC0big7XGRpg0qeNyiboZ5iqf61JKKVUCFi+2gdO8ebDttvDgg3DUUbYPuFJKqS7RrQOo2CApNtsCmRVQcByon7WSuivehyVL4MEHcU7YjvoDvyCwbGS7YCn6byDQ9jibKnfapU4ppVTOVq6EyZNh1izYZBM7YfuZZ9oDSoaCK4I6BkoppfKg2wZQicYvRTM6tbUZdOl780274L3LmGWeJiRjmOU7nfozPDZzk+bzpk1rP14pXZU77VKnlFK9S14CljVr4Npr4c9/tge1iy6C3/0OBg3Kqg2V/SqZ+MTE1ip89TX1GkQppVSOum0AlWrcU8pszxtv2AG3Dz8M/fsT+Mn9hF7sSzgshJqTj5+K/7w5c7Ifd6Vd6pRSqncIrghSXVede8ASicCdd8Jll9l5nX71K1sgYuTInNogIkRMhIiJ6DxQSinVSZ5iNyATwaBNFgWDbc9FgySvt2OQFC1rXl1t/3Uc4L337AFo553hmWdsEPXRR/ivORifTzqsJ/4z4z9v/Pjkn6+UUqp3CywLEAqH2k1cm7EFC+yB6/TTCe4+lNp7fkNw6jlZBU/xbYhEInjFi1e8+Lw+KvtVUttQS3BFMP2KlFJKtVPyGahkE8Km6hLXrqz5cxFGP3YNztzJUFEBl15qu0BsuimQeD3JPjN+uXxVzFNKKdWz+Ef68Xl92U1c+/XXNuM0fTpsvjnB26dQ/dk1hN57E9/SmVlnseLbMO2gaTSua9TufEop1UklH0Cl6qqXrEucfY+x3fLCYQJz1+KcfbbtN7755h2Wj11PMAhTpkBTU8cJbeM/T7vkKaWUSiSTiWtbx0htvS/OU+/YC3yrVtkrgFOmEHjtFkIft89iZRPoJGtDbUNth+yYBlBKKZW5kg+gsqleFwxCYF4LlYuewhf2E6Icn9fgf+AcOHKL9ssFEmeuqqvbgicRKCvT7nlKKaWyl2ri2tbxSS1N+MJQPzOCs+MYuOUW272BHLNYGbQhH+tVSqnerOQDqEyr1wVfNFTvF6EpBB7GcsG2jzLo8H3xHzMEx2kfPCUrcR7NdkUi9rEx9qaUUkrlIlklvsAH9YSaNxAWQ0ggcPExOOfe124+p0yyWLko1HqVUqq3yCiAEpGDgBsBL3C7MWZqQVsVJ76rXIcM0pIlBE4O0hQ6nghlRDDcsPxonj2mY8CVSfW+DRvaAqdwOLMKe0oppVSspJX4Xn8d/x/q8O1tCJUJPl8F/iPPTzgZbqosVmcUar1KKdUbpK3CJyJe4BbgYGBH4DgR2bHQDUsmmkGaPBmqqw3BX98Mo0fjXzkbj0cAA0hr4BMvXfW++no44wxbb0Ir7CmlVPchIgeJyBIReV9ELi12ezpU4lv6jC1FXlWF89Zq6neo5fSqMzhx55OK3VSllFJZyKSM+R7A+8aYpcaYEPBP4IjCNiu5dhmk9WEC93wCJ5yAs/RubrnVS3m54PHYAChR4BMNkq66KvHkt44Dt94K8+cnX0YppVRpKbWLfdA21sgrXnyecvxX3w2XXw7jxsFbb8G+P2XWa7O4bdFtVNdVa0lxpZTqJjLpwjcMWBHzeCWwZ/xCIjIBmAAwYsSIvDQuEf+PvsbHRoTw4JMW/NN/BafvBMCECXbsbV1d6nVkUj1PK+wppVS30nqxD0BEohf73i5Wg5zhDvUnzCNw/zX4b30cZ/UXcN998ItfABBouE2r4SmlVDeUSQaqY6ds20+u/RPGzDDGVBljqjbbbLPOtyyRRx7BOXF76uVnXDW2gfpAGY4bPMWaNQtuu8129QvqBT2llOoNEl3sGxa/kIhMEJEFIrLgyy+/LGyLVq3CueAvTLr4UZzv7Q9vvtkaPEFchsqthhdcEdQJbpVSqsRlkoFaCQyPebwV8ElhmpPE2rVwwQUwYwbsthvOXdNxdtgh4aKpikQopZTqsTK+2AfMAKiqqipcndVXXrHB0sqVcO21cOGF4Gl/zTK+Gh6QuOiEUkqpkpJJBuoV4Hsiso2I+IBjgUcL26wYr74Ku+1mU0qXXAIvvghJgidIXSRCKaVUj1X8i31R99wDY8bYcq4NDXDxxR2CpyhnuMOkMZNwhjsdi04sC3Rtu5VSSmUkbQBljGkBzgGeBN4B7jfGvFXohhGJwJ//DHvuCWvWwLx5cM01BBf6qK1N3jUvXZEIpZRSPVJxL/aBPW5NmgS//jXstRcsWGD/zVCiLn1KKaVKT0bzQBlj5gJzC9yWNp98AiedBE8/DUceabNPlZUpJ8GNpQUglFKqdzHGtIhI9GKfF7izSy72Ra1ZA8cfD48+aufCuOkme6DKgk5wq5RS3UNGAVSXW7LEppimT4fTT2+dXFDHNymllEqmyy/2RX34IRx+OLzzDtx8M/zmNwknxc2ETnCrlFKlrzQDqP32g2XLoLKy3dPR8U3RDFTs+KZg0AZUfr8GVUoppbrIs8/C+PH2yt4TT8DYscVukVJKqQIrzQAK2gVPscHRtGkwZ449XkUDpUy79imllFJ5s3YtHH00DB4M//43fO97xW6RUkqpLlC6AZQrNjjyem2viJYWW9ho9GgbKGnXPqWUUl1uo41s4PSDH8CgQcVujVJKqS6SSRnzoooNjpqbOwZKoKXLlVJKFclee2nwpJRSvUzJZ6Bixz3FZqBiA6Vo6XIdA6WUUkoppZQqpJIPoOKDI0gcKGnpcqWUUkoppVShlWQAFV9RLz440kBJKaVUKQmuCOr8TUop1UuUXAClFfWUUkp1J8EVQarrqgmFQ/i8Pupr6jWIUkqpHqzkikgkqqinlFJKlarAsgChcIiwCRMKhwgsCxS7SUoppQqo5AIorainlFKqO/GP9OPz+vCKF5/Xh3+kv9hNUkopVUAl14VPK+oppZTqTpzhDvU19ToGSimleomSC6BAK+oppZTqXpzhjgZOSinVS5RcFz6llFJKKaWUKlUaQCmllFJKKaVUhsQYk/+VinwJfNSJVQwGvspTc0qBbk9p62nbAz1vm3R7Slu227O1MWazQjUmU3qs6qCnbQ/0vG3S7Sltuj2lLZftSXi8KkgA1VkissAYU1XsduSLbk9p62nbAz1vm3R7SltP255M9bTt7mnbAz1vm3R7SptuT2nL5/ZoFz6llFJKKaWUypAGUEoppZRSSimVoVINoGYUuwF5pttT2nra9kDP2ybdntLW07YnUz1tu3va9kDP2ybdntKm21Pa8rY9JTkGSimllFJKKaVKUalmoJRSSimllFKq5GgApZRSSimllFIZKrkASkQOEpElIvK+iFxa7PZ0hojcKSJfiMibxW5LPojIcBGZLyLviMhbInJesdvUGSLSR0ReFpHX3O35fbHblA8i4hWRV0XksWK3pbNEZJmIvCEii0VkQbHb01kiMkhEHhSR/7n/j5xitylXIvJ993uJ3r4VkYnFbldX0WNV6dJjVfegx6rSpceqDNZbSmOgRMQLvAv8DFgJvAIcZ4x5u6gNy5GI7At8B9QZY3Yqdns6S0SGAkONMYtEZACwEBjXjb8fATYyxnwnIuXA88B5xpj/FrlpnSIiFwBVwEBjzGHFbk9niMgyoMoY0yMm8hORWUCDMeZ2EfEB/Ywxq4rdrs5yf7s/BvY0xnRmYtpuQY9VpU2PVd2DHqtKlx6r0iu1DNQewPvGmKXGmBDwT+CIIrcpZ8aY54Cvi92OfDHGfGqMWeTeXwO8AwwrbqtyZ6zv3Ifl7q10rijkQES2Ag4Fbi92W1R7IjIQ2Be4A8AYE+oJByRXNfBBbwieXHqsKmF6rCp9eqwqXXqsykypBVDDgBUxj1fSjX/0ejIRGQnsArxU3JZ0jtuFYDHwBfC0MaZbbw8wDbgEiBS7IXny/+zdeXhTVfrA8e+btCkgIFrHcVAQxJ+7M+rgcp1Bo3XfRgdn3IsruKDiMo44LrjWccXRccEFqSgugzouKGg1DGrccGPUcUcBxQUFUWhDk/P749y0aZrlZmmTtu/nefK0SW5uTm7ae+97z3veY4BZIjJXREaXujEF2gD4Fpjspq3cISKrlbpRRXIoMK3UjehEeqzqIvRYVbb0WFW+9FjlQbkFUJLisS59laU7EpG+wHRgnDHmx1K3pxDGmKgxZitgPWA7Eemy6Ssish/wjTFmbqnbUkS/M8ZsA+wNnOKmGnVVFcA2wC3GmK2Bn4EuPXYGwE3vOAB4qNRt6UR6rOoC9FhVnvRYVfb0WOVBuQVQC4FBCffXA74sUVtUCm7+9XTgXmPMw6VuT7G43dMhYK8SN6UQvwMOcHOx7wd2FZGppW1SYYwxX7o/vwEewaZOdVULgYUJV47/hT1IdXV7A28YY74udUM6kR6rypweq8qaHqvKmx6rPCi3AOo14P9EZKgbKR4KPFbiNimXO5D1TuB9Y8x1pW5PoUTkFyIywP29N7Ab8L/Stip/xpjxxpj1jDFDsP87zxljjixxs/ImIqu5A8Bx0wf2ALpslTBjzGJggYhs7D5UA3TJQe1JDqNnpe+BHqvKmh6rypseq8qbHqu8qSjWiorBGNMsImOBmYAfuMsY826Jm5U3EZkGBIG1RGQhcJEx5s7StqogvwOOAua5udgA5xljZpSwTYX4FTDFrcriAx40xnT5cqrdyC+BR+y5EBXAfcaYp0vbpIKdCtzrnnR/ChxT4vYURET6YCvRjSl1WzqTHqvKnh6rVGfSY1WZ64hjVVmVMVdKKaWUUkqpclZuKXxKKaWUUkopVbY0gFJKKaWUUkopjzSAUkoppZRSSimPNIBSSimllFJKKY80gFJKKaWUUkopjzSAUkoppZRSSimPNIBSSimllFJKKY80gFJKKaWUUkopjzSAUkoppZRSSimPNIBSSimllFJKKY80gFJKKaWUUkopjzSAUkoppZRSSimPNIBSLUTELyI/icjgYi7bFYnIbiIyvxPe5zIRubuI6xsrIt+4383qxVqvUkp1BhEZIiJGRCrc+0+JyCgvy+bxXueJyB2FtLdcFbptcnifo0XkhSKu7yARWeAew7Yu1nqVKjYNoLowdwcTv8VEZGXC/SNyXZ8xJmqM6WuM+aKYy/Z0IjJVRCLu9/K9iMwSkY3yWM9CEQlmeL4XcA2wi/vdLCug2V2WiJwuInPdbZ725EhELnVPMIKd2DylujURmSkil6R4/A8isjjXE3pjzN7GmClFaFdQRBYmrfsKY8zxha67uxORCSKyyj2GLRWRl0TEyWM9IRHJtr2vAca6x7A382tx1yYif3a38QoRCWVYbpR7DNO/4RLQAKoLc3cwfY0xfYEvgP0THrs3efmOvhKlMrrC/Z4GAd8Dd3XAe6wDVBlj3s31hSLiE5Gy3h/k0MZFwCXA3RnWtRHwB+Cb4rROKeW6GzhKRCTp8aOAe40xzZ3fJFUED7jHsF8ALwAPp/iOi2F9IOdjGNjMmCK3peg8tvF7YCJwZYb1rAGMJ89tpQpX1idMqjBuetgDIjJNRJYDR4qIIyIvu1eRvhKRf4hIpbt8hXs1Y4h7f6r7/FMislxEwiIyNNdl3ef3FpEPRWSZiNwoIi+KyNFp2r2DiLwhIj+KyNcicrX7uE9E/uVexVzqXs3aNOF1U0XkJvcK6E8i8h8R+aX7fktF5H0R+U3C8gtF5K/u4z+IyJ0iUpWmTeuJyCMi8q2IfCYip+TznRhjfgamAVukeZ8DReRdt73PicjG7uPTgIHAU+5nOzPpdZvi7kjd52e5v/9eRF53t/urIrJ9wmtecHthwsDPQLt0zGzbSEROFJGPRWSJiDwqIr9yH79cRK53f68SkUYRucK939e9v7p7/3cJf5NvichOubQxxTb+lzHm39iDUDr/BM4GVmVbn1IqJ48CawIj4g+4J3v7AfXu/X1F5E13H79ARCakW5kk9FqITR2/RkS+E5FPgX2Tlj3G3VctF5FPRWSM+/hqwFPAQGnN0hgotmdlasLrD0jY/yYfX+aLyNki8o67P31AbK9/qjZvKCKz3eW+E5EHEp67wf3MP4rtKU/cThNE5CH3WLZcROaJyEYiMl5savYCEdkjadvUufv2ZSLybxFZM02bVnf331+JyCKx5wc5BxzGmFXAFOwFu+oU77OjiLzmtuc1EdnRffxy7N/ETe72vynpdVUi8hPgB94WkU/cxzd1P+dS97s5IOE1d4vILSIyQ0R+BnZJ0Z6M2yjdd+7+LT2esNzHIvJgwv0FIrKV+/smIvKM2AyTD0Tkz7m0McU2ftYY8yDwZYbF6oB/AN9lW5/qIMYYvXWDGzAf2C3pscuACLA/NljuDWwLbA9UABsAH2K7y3EfM8AQ9/5U7D/ncKASeACYmseyawPLsVf8K4EzsSeuR6f5LK8Bh7m/9wO2d3/3AUe7j/UCbgJeT3jdVGyPwtbu87OBz4DDsTvlK4FnEpZfCLwDrAesBbwMTHCf2w2Y7/7uB94CzgMCwIbu9q5xn98Z+C7DdzM1Yb393G3zfMJ3dLf7+6bAT8Cu7nY6z/1+KhPaG8zwPhvaf+mW+2sBy4DD3O/rSGAJsIb7/Avu59jUfb+KFOvMtI32cLf3Vu72vhl4LuG5N93fdwI+AV5MeG6u+/sgt017ut/vXti/o+p0bQT+Bjzq4X/iSuCOFI8fBkz3sk31pje95X4Dbk/83wPGAG8l3A8CW7r/878GvgYOdJ8bgj22VLj3Q8Dx7u8nAv9z9xtrAs8nLbsvMAwQd7+8Atgm4T0XJrVzAq3HqY2wF2l2d/c15wAfAwH3+fnAq9gLWWsC7wMnpvn809z9lM/dN/4+4bkjsYFHBXAWsBjoldCeRnd/WIENOD9z11UJnAB8lrCuELbHfQtgNWB6wudJ3o6PAre5y63tfpYx7nODgaXA4DSfJ3E7VQFXAwvc+0cDL7i/rwn8gO1trMDua3+gdX/e8l1m+NsxwIbu75XudxA/9u6KPZfY2H3+buwx7nfxbZ1ifZm2UdrvHHt+tNRd76+Az4FF7us2cD+Xz13nAuAY9zNvgz2GbZ6ujdhzknc8/B8dD4RSPL4d8Lq7vqzbVG8dc9MeqO7vBWPM48aYmDFmpTHmNWPMK8aYZmPMp8Ak7IEmnX8ZY1439qrTvdiT5VyX3Q978Py3+9z1ZL5qsgr4PxGpNsYsN8a8AuB+hrvdxxqxO/XfulcX46YbY950n38U+MkYc58xJooNXJIHpf7DGLPQGPMdcAV2h59sB6C/sfnyEWPMx8CdwKFuu2YbY9bK8HkAvtMGpgAAIABJREFUzhWRpdiAqAo4NsUyhwKPGWOec7fTlUB/bMCbj/2Bd40x09zveyqQfNX2LmPM+8aYVSZ9ak26bXQE9iTpLXd7nwvsLCLrAS8Cm4nIAGwANQkYKiJ9sH9vs9111Lqfeab7/T4NvI0NpFK20RhzuTHmwHw2iIj0By4Fzsjn9UopT6YAfxKR3u79WvcxAIwxIWPMPPd//h1swJHpOBT3Z2CiMWaBMeZ77FX4FsaYJ40xnxhrNjCLhJ6wLA4BnjTGPOPuf6/BXnTcMWGZfxhjvnTf+3HSHw9XYVPRBhpjGo0xLUUWjDFTjTFL3H3ZtdjjwcYJr53j7g+bgYewKXNXum26Hxji7lfj7jHG/NfY7IYLgD8n9yyJyC+BvYFxxpifjTHfYI/D8WPYF8aYASbzmOY/u8ewBcBvgVT74H2Bj4wx97ifbxo24N0/w3oz2QHoi/38EWPMc8ATtD1O/9sY86L7t9SYZj3ptlHa79w9P1qO/Y53BmYCi0RkE/f+HGNMDHt+M98YM9n9zG9gg7SD07XRPSf5dT4bxG33zcCp7vurEtEAqvtbkHjH7Wp+Umwa3I/YsSKZTv4XJ/y+Arszy3XZgYntMMYY7JX/dI4BNgM+cLvd93Hb7heRq9zUjB+xV4pIav/XCb+vTHE/uf2J2+dzt63J1gcGu138S92DyDnYFAavrnQPUL8yxhxojPksxTID3TYANmDEbqd1c3iftOtzfZ60vgVkl24bJbf3R+xVuXXdA9Wb2OBpJ+xVspcBh7YB1PrAYUnbdgfafg9e2ujVpdiATIufKNVB3IDhW+APIrIBNvPhvvjzIrK9iDwvNiV6GbZnKdtFKEg6lpC0fxObKv6ym0q1FNjH43rj607e/y6g7f7S6/HwHGwv2KtueljLBTMROUtsmuEyt42rk/kY9p17ATB+n6T3Td4elbT/zOu7j3+VsJ+9DdsT5dWD7jFsbWPMrsaYuSmW8XLMycVAbE9XYqBQjGNYfBtl+85nY3sud3J/D2GPX8nHsO2TjmFH0Pb8oJjHsJOxvVfhIq5T5UEDqO7PJN2/Dfgvtou8P3Ahdkffkb7CpoABICJChh2qMeYDY8yh2J37tcB0sbnmtdgD4q7Yg86G8VUW0LZBCb8PJnXO8QLsVbUBCbd+xph8r6ql8yV2ZwzYMV/Y7bbIfSj5u8xpfa7BCevzus502yi5vf2ANRLWPxubCrklMNe9vzf26uUcd5kFwOSkbbuaMebqHNvoVQ1whnsBYTE2NeNhETm7iO+hlLLpZ7XYdK5ZxpjEwOA+4DFgkDFmdeBWvO3Hv6L9/giwY2iwV/6vAX5pjBkAzEhYb7b9SPL+TNz3WpT2FWkYYxYbY04wxgzEpi/eLHZc1Ajgr9ietDXcNi6juMewVbTP8FgANAFrJexn+xtjNi/gfVPJdszJ5xg2SNoWDyrGMSy+jbJ95/EAaoT7+2zaB1ALgNlJx7C+xpiTcmyjVzXAQQnHsB2Ba5PHlKmOpwFUz9MPu8P+2R0sOaYT3vMJYBsR2V9sJcDTsWkJKYnIUSKylns1aBl25xPDtr0JO2amD3B5Edo2VkTWFZFqbEWbB1IsEwYi7pXDXm5P2JYi8tsivH+iB4EDxJbbrQT+gk0heMV9/mts7rVXTwCbi8ghYot+HI4NOmfk2K5022gacJyI/No9eanDpjXEexdnY/Pj33HTUULAaOBDNwUG4B7swWB3d7v2EpFdRCRVT6An7mfthR27Fl9nPKVlZ2xAt5V7+xqbZ35rvu+nlEqpHnsB5QQS0vdc/YDvjTGNIrIddkyIFw8Cp4kt6rMGNm04LoBNh/sWaBaRvbHjLeO+Bqol/fx4DwL7ikiNu/89C3u8eclj21qIyJ/cVGawvfIGiGI/d7PbxgoRuRCbpl2II0VkMzc9+hJsKn00cQFjzFfYdMZrRaS/2IJMw0TES9pkLmYAG4nI4e5++BBsNskT7vO5HsNewY5ROkdEKsVOObE/NpUxF+m2UbbvfDa26ENv97g2B5teXo3NsMD9bBu55y2V7m1bSShAkqv4sRA7psrnHsMq3aePxo4Jjh/DXgcuxo6TU51IA6ie5yxgFPbE/DZSBwxF5V55PAS4Dhv8DMPufJrSvGQf4H2xlQOvAQ4xxkSAydgrRl9iK87lfGBLYRrwLLbIwQfYMT7J7W9227QddiDxd9ht1x9a5hdZWmhDjC0/Pgq4BXuA3Qs4wM3Nxm3bxW6awDgP6/sWOAB7xXMJdtzPfgnBi1cpt5Gx45UuAR7BXhkejE1diHsBG+j+x73/DvbKX/w+xpj5wEHYvPRvseX4zyLDvklELpCE6kgpTMCmupyNPdisxAZ+uGMPFsdv2MD8e2PMT5k3gVIqF+7/9kvYQfaPJT19MnCJu4+/EHsi68Xt2LEobwNvAA8nvN9y4DR3XT9gg7LHEp7/H3Zf9qm7D21zkcYY8wG2wMON2H38/tipQSIe25ZoW+AVsVXlHgNOd9O2Z2KrAX6ITR1rpPD0rnuwhQoWYwsUnJZmuVpskPkedvv8C9sDj4gMFlsZL2uV00yMMUuwY4LOwh5zzsEec+I9YjcAB4ut6PoPD+uLYI9he2O/k5uBWve7zEXKbZTtOzfGfIgt7DTHvf8jdhzxi/Eg1f272wM7nuxL9z3+jg3mUxKRI0QkU/nxo7DHrVuwvV8rsX/7GGOWJh3DIsCPpofO+1hKYoejKNV53N6AL4GDjTFzsi3fge1YCBxpjAmVqg3lTreRUkqVJ7GTrE41xqSdMLyn022kOor2QKlOISJ7iZ2Hogrb29CMLaOqlFJKKaVUl6EBlOosv8d2fX+HTU070BiTLoVPKaWUUkqpsqQpfEoppZRSSinlkfZAKaWUUkoppZRHFR2x0rXWWssMGTKkI1atlFKqi5s7d+53xpi0Uxl0Fj1WKaWUyiTd8apDAqghQ4bw+uuvd8SqlVJKdXEi8nmp2wB6rFJKKZVZuuOVpvAppZRSSimllEcaQCmllFJKKaWURxpAKaVUDxQOQ12d/amUUkop7zyNgRKRM4DjAQPMA44xxjR2ZMOUUkp1jHAYamogEoFAABoawHFK3SqllFIqwZdfwnXXQVMTbLONvW22GVRWlrpl2QMoEVkXOA3YzBizUkQeBA4F7u7gtimllOoAoZANnqJR+zMU0gBKKaVUmVi6FP7+d7jhBli1Cqqq4Kab7HNVVbDllq0B1Tbb2Pu9erW8PLwgTGh+iOCQIM6gjjm4ea3CVwH0FpFVQB/gyw5pjVJKqQ4XDNqep3gPVDBY6hYppVTxdMYJtOoAxkB9PZx5Jnz/PRx+OFx6KQwZAh99BG+80Xp78EGYNMm+rm9fmDEDRowgvCBMTX0NkWiEgD9AQ21Dh/wNZA2gjDGLROQa4AtgJTDLGDMreTkRGQ2MBhg8eHCx26mUUqpIHMem7YVCNnjS3ielVHfRWSfQqsgWLoTRo+Gpp+D3v4cbb4Sttmp9fuON7e2ww+x9Y2D+fBtMnXsujBoFH35IaH6ISDRC1ESJRCOE5odKE0CJyBrAH4ChwFLgIRE50hgzNXE5Y8wkYBLA8OHDTdFbqpRSqmgcRwMnpVT301kn0KpIjIHJk+GMM6C52abtjR0Lvix17kRg6FB7q6iAAw+EBx8kOCJIwB9oCaCDQ4Id0mwvKXy7AZ8ZY7617ZWHgR2BqRlfpZRSSimlVCcKDumcE2hVBAsWwAknwMyZsPPOcOedMGxY7uvZf3/YdFO46iqcw96kobahLMZAfQHsICJ9sCl8NYBO3a6UUkoppcqKM8jplBNoVQBj4I474KyzIBazBSJOOil7r1MKLePdTjsI56QrYNYsnD337PDv3csYqFdE5F/AG0Az8CZuqp5SSimllFLlxBnk9OjAKbGIBpA2mCxJsY0PPoAxY2D2bNhlFxtIbbCB55cnf7Y24922Xgvn73+HPffsoMa38lSFzxhzEXBRB7dFKaWUUkoplafEIhp+nx9BaI41tyuokanYRocEVpGILU1+2WXQpw/cfjsce2xLr5OX90xu86jfjGo73u1PQZzznoLXXoNtty1Ou9PwWsZcKaWUUkopVcYSi2jEojEADKZdQY10xTY6pIrhiy/aCnvvvQeHHAITJ8I667Q87fU9k9sMtB3v9scz4aqwDdT+9a/C2pxF7smGSimlylY4DHV19mc5rUsppVTHixfR8IufSn9ly+/JBTUSl0t8LlVglbclS+zYpt//Hn7+GZ58Eu6/v03wlO09wwvC1M2pI7wg3K7Ntb+ppaG2gUt3udQGXRvvBiefDA8/bAtUdCDtgVJKqW4iHIaamtYJchsaMpQq//hjWLYMNtsMevcubF1KKaXKQnIRDbABSnWf6pbAJD5GLFWxjaJUMWxqgltugYsvhh9/tBPjXnyxnfA2hXTvmapnKlWb2/RWjRtnS5oPGpR7u3OgAZRSSnUToZANeKJR+zMUShH0vP46XH45PPqove/zwYYbwpZbtrmFnhtGJOLLvC6llFJlJ1URjVQpcqmWK6iKYWOjLUV+5ZV2Ytw99oDrroPNN8/a3lTvmapnavyI8Znb9Itf2FsH0wBKKaW6iWDQ9hbFe42CwYQn330Xzj4bnn4aBgyACy+0wdK8efb29ts27cHYedCDgZ0JmKeJ+KsIBKTtupRSSnUZuU4unHMVw8ZGW03vyith0SKbsjd5sk1jEMn40sTiEeNHjG/zXDnP6aUBlFI9TDhsexOCQe1R6G4cx6batfl+ly+3qRM33AD9+tlBTSefDP372xcdfHDrCn7+2Q7ynTcPZ948GqaMJESQ4COnAwHq6vTvRimlupoOC0RWrrQ9TnV18OWXMGIE1Nfb8uRZAifIXjyinOf00gBKqR5Ex7V0f47jfqfGwLT77USFixfD8ccTPugqQm8NIPhumu99tdVs6Ve3/Kuz10ycvfYi/PgG1Nw8Uv9ulFKqCyp6IPLppzBpki1F/v33sNNOMHWqvcLmIXCK89IzVq5zemkApVQP4mmMjOr63n0XTjnFTlQ4fDg8+ijh6HY5B8/hfnsQGnIrX9zyE5GIIRqVsv67EZEBwB3AFoABjjXGaA1BpfJUkolWPSjXdqVTrPZ6nSA3lYIDke++gwcfhHvvhZdesuNnDzoITj/d9jzlIZ+esXL57jWAUqoHyThGRnlWtmmQy5bBJZfYdL3VV4dbb4Xjjwe/n1BdbsFzOAw1uwmRptH4Y01UVETBX1Hufzc3AE8bYw4WkQDQp9QNUqqr6pD5gLpRu7yeyBervV4nyC2qFSuYdM/pTP/g34ycs4TRr8dgiy3sWKfDD2+pdJdvUJNrz1i5fPegAZRSPUrKMTIqJ+WYBhme00zo2rkEQxNwfpxpg6YrroC11mpZJtfguaW3MiZABSdU3MPgvx5OcM+qkn/eVESkP7ATcDSAMSYCRErZJqW6slwLD/SkduVyIl+s9nqdILdgjY2En7iF0JypLP1oHldttwr6w6x9gfMvYPQfLmmzuJdtkSnAyqVnrBy++zgNoJTqYVrGyCgg996kjkqDzLdXK/yP16gZtwUR81sCvkdouOtznKM3brdcrsFzm4CrQqhtnIRTtRic8ZlfWDobAN8Ck0XkN8Bc4HRjzM+JC4nIaGA0wODBgzu9kUp1FeVaAa0c2pXLiXyx2pu4nuQeqIK2gTHwwQcwcyY8/TThj56j5tAIkQFgtgUShjRN//EVu/NMkG1bFLPXqBy++zgNoJRSPVY+vUkdkQaZ3I6JE+0E7hkDnQ8/hLPPJvT45kTYmigVNOFnwrSNmbBx6tflEjy3Dbj8OJdXw9VX2wp+q6+e5yftUBXANsCpxphXROQG4FzggsSFjDGTgEkAw4cPN53eSqW6iHKogJaq56Ic2pXLiXyx2pu8nnnfzGP6e9MZudnI3Na5YgW89hq8/LI9+ITD8M039rmNNyY08rdEKl8hSgxBsMNJrZGbjWy3uuTA7otlXxBeEM44l1OxtkEpe0TFmOIfP4YPH25ef/31oq9XKdVW2Y7F6SLq6uCCC2xvkt8Pl14K4z10sCRud/D+HaT7vhLb4fPZtsRiaYK6L76Ayy6zc2z07k34yH9Sc/eRNDUJsZh9fVVVcVIL27S315twxhm2ZO2wYQWtV0TmGmOGF9a6dutcB3jZGDPEvT8CONcYs2+61+ixSnVH5TLIvlDlNN4llVJuZ0/bprnZHi8++cQWFnrnHXjrLfszGrXLbLQR4Z2HEdqkiuDvj8LZ7o/t1n3q9qfy1ldvMXKzkYz+bXL/U2t76t+uZ/Jbk9uNyyr37zGbdMcr7YFSqkB5p14VGPxMmgRjx9r9YLFOmHuafHuT4j05ufRgZVo2sR0i9rhnDDQ1JaQI/ve/cO21tgKSCIwZA+efj7POOjQcBRMmwLPP2sCrGKmF7du7NU4olP8KO5gxZrGILBCRjY0xHwA1wHulbpdSnamrn6wmyrnnwhi784xG7e/xDoLkn6keS3zO74eKCvvT77dXpVIoWXltYwh9NKt12zQ3EXr4WpzGbVsDpk8+gfnz7faI++Uv4de/hnPPtQeHHXYgvOJD+/fyU4TAMzNp+FVDXr08ziCH0PwQzbHmdt9XOfUaFZMGUEoVIN+CAoUWIgiHbZXq+L6xzYm28qzQohpexkNNmgTTp0OfPumXTWzH0qVw1VX28VjMUP31e7DnmTBrll3J6NFwzjmQMH7HcWwANWdO8VILu2jJ+1OBe90KfJ8Cx5S4PUq105E9FyUfZL9iBfzwg60Imu62YgU0Nqa/rVwJq1YRHPATgV1iRHwQiMUInnYdfDnR7pTigVLizw7IqALsBavEoCqfn5kei6ccRKPtb83NdoLz5cvt7aef4KefCP4qSmAUrdvmmumwcDoMGGAzBLbZBv78Z/v7sGGw6aaw9trtPlrovdR/L/kEh5nSGst1LqdCaAClVAHyPclMfF1Tkz35nTDB+wlqKGT3t3F+f24nzKXqNetI+batkKIa2XqwJk2yHUVxlZX2Z6pl4+2oqwOfzxCLCT6iLLlhKqzzDlx+OZx4Iqy5ZtrPUcwKi12x5L0x5i2gqKmBShVDPGiq7lPNuKfHdVgPUYcNsl+1ChYsgM8+g4UL4auv7ATdX33V9veffsq8Hp/PXgjq1cveevdu/b1XL+jbF6qrIRDAqaigYdFGhPp+SzAyEGf4QG8BS2KvkQjh6OeEzKcEfcNwKoa0tiU+4WviT2PaBi+JP9MFbtl+Jv4eiaR+Lp67nXiLf57qahgyxG6bfv2gb1+cfv1o6PM9oaovCQ7aCefY3WwP02qrZdz8ycF7Mf9eumtPUzoaQClVgHxPMuOva2qygdCzz9reA689UcGgTdtrarL73Ztuan1dtkCio3rNShlclaq0eLagZfr0tve33hoOPDDNNjIGXn+d4JsvUBUbQ4RKAtJM8LzfwwUT7BfuoT3F+txa8l6p4khMqxMRYiZGzMQ6pIeo4JPYJUvg/fdbb++9B//7nw2eEq/agT2ZX2cd+NWvbI/HOuvY2xpr2EIzqW59+7YGLF4+j3vLl932E9wAYTYNR5R/SqPXHsp02ybd69OldxYz6OmOPU3peAqgdHZ3pVLLdpKZLqiIvy7fcSvp3tdLIBEKtQZujY1QX194+e5Sz41UynSz+PvEhwclvu/IkTbzLu6442wGXotoFF58ER5+2N4WLMAJBGio+Z7Q+qMIHjcMZ8fUNRA6I2DVkvdKFS4xrc5nfC0lqDuqDLPnk9ivvrLV2OK3N99srcYGtndok01gxx1tGtjQofY2aJANlPr2LXrbi63kKY05yncMm5ceznTboicFPcXktQdKZ3dXKkniCWyqym3ZgopSjVuprm69kGiMTTPbeuukE/sUMvW2lXq8THW17YkzprVt6QKMfCvopZPpe45v0+nTbTA1ejQ2Feb5523A9Oij8PXXtndpjz1sGcD998dZc820V13DYRv0Tp5ssz/KZTJfpVRqyWlSE/eayJIVSzo3zWnlytay1fGAadEi+5zfD5tvDvvua39uuqm9rb9+2gIKueroinXp1l9O8wZ5kU/A57WHs6tti3KXNYDS2d2Van8y7rWnJ1tQkW+aVOL7+/1w7LFQW+stpXDJktZUb7DB1NixsOWW2XvQ0rW1kPEyXnpS4kED2M+ZHBCNG9eaRj5xon081feTvN3iFe8KCULSfc8tn2tnw+jd5xO+413qtl5B8JM7cZbPsrnq++xjI6t99rHpMB62VU2N7TmMf39dqMCDUj1StjSpDgkuIhF46SWb4jB7Nrz6qn0MYMMNYeedYdtt7W3rre24pA7S0ZUBM60/3xS1UpUozyfI8drD2dPGKHU0Lz1QG6Czu6seLFWw5CU4yhRUJAcNhVR/i0bhtttgyhTbtmwBWTBoixlEEi6DRKPtP0O6IDHdBK1eA8HkHqBs46rq6+3UQ6tW2ccmT7YdOImpc5GIDQRFbICY/P3U19vHvvii9fHEXrhcUhmTtfued4wQvusTak7ckMgqHwGJMNFcwThuIEKAgP8PNFz5Cs5p29oUmTTbJlVb4p8rHjyJdJ0CD0r1ZOnSpIoaXHzxBTz9NDz1lN2ZLl9urxT99rdw2mk2aNpxx7SFaDpKcq9K/dv1RT2Jz9Zrk2uKWi7fSbEDrXyCnFx6ODVdr3i8BFA6u7vq0RJPxuMn2rW12Xtc4kFFvOckrhjjheIn7fGeiMQg4JZbMhd3cBx7/6qr4PHH7Wurqtp/hlzT8rwEgsmffdSo7OOqEntboP1y6QLV+GN+f2u6W7ywEdjfjbGBmTF2mVS9W2nHt71kCD2+nOB6H9Mw9nNCIUNw2WM4u99H3aqziHApUfxETCXTNzmfyIe9icaECBWEYjvh9E5an4e/i8TPmtjzqL1PSnVNBY3RaWqyOeBPPWUDp/fcac8GD4bDD4e99oJdd4X+/TvuA7gyBRKJJ/h+nz/lZKuFKHZqmtfvpKN61nINcrRnqTS8BFALgYXGmFfc+//CBlBKFaTcSmKna08waE9W43PzxU+0vfa4TJliT3jjPUSJRRwyzd+UafskBmfx3hlj7O+JJ9TpUv0cBx55pH1vUF1d6/sVq4x14nskB2WQ+j3CYTs+rKmp/dQeyW1J1/sVf+yLL+D221snXj/hBHt+EQza7Xfbba3zL7Z8F8YQblhBzf69iUSEQEWMhtP+jROYCx9/TPjNXtR8dAsR+hBgExo4hfFrfADDh8MfzyLYd3cCl/mIrDIEAhWMPGN95ozLvC07MuVTKVWevJ78twQoa2+L8/b3dgzljBm2lykQgJ12slVq9t7bFn7IodJdobIFEokn+F8s+4Lb37i9qEUdih1AeP1OyqlAhfYsdb6sAZTO7q46QqmrtuXanq22smNuE0+0x4/P3uZUJ8WJRRxiMXvfc3tWrbIzrS5dilOxFOfPP7H4rU149OV1AGHVKkP9X+bh7P8UrFxJ6PkdiDTuTtT4iUYNt91qmHL7Khp2vQJnjf/hNDfjNDcTfnxDal65nEiskoBvFQ2bn47T7780bLAZoWVbE+w3F+e0d2yDjWn7M/57PNLx+ezBW4Twit8Q/PROVlFBJc3cOPhqArGziVBBwDRTO+dUaocJoeW/JbjG2zjnf0B4+RbUzP07TbFKYvgQDH6Jst+v3mCdPsupHfYizm2fwR2t82U47o2H/PCwn/DioYQWDiM45HPw+Zgio4iIj4BEqV16K47/Q5gcgYWDmOI7h0jUT8A0E7z1aLj+OfjhB0LNZ7u9SBVEIjFC17yG478ahg6lvmkijfTC4CPi8xE6+ymcK1e3n9kNGCfeYNMJ44HOlltmT630ErBqZTylyp/nUtQeTv7DHzZQc/8+RGIRAs3QMAWcpl/AoYfCAQfALrtknf+nI3kJJOIn+OEFYaa8PaXohQyKGUB4Dci0KEPP5rUKn87urooq1/SwYknXq5OpEEBNTWtPiM+X33xPiSfFoZBdjx2zY3jzhRXw63mtExEuXkzoqd8QWfkHmwK2spnQXtfgRC+zM5K3Mx04qOXe4hc/hhdtJ3GwYgQVJkgUAcSe8Ef9hOb2w1n7nZbZ0EPf7kEkVmHfL2YI/bwtzi8/xVntc5z1FtiAyLe2+9PX+jPx93hlioQAq/7NI4gQAIQIPt5kGxq2P4/Qki0JrvkOTv/PwRicX3zsziocJbR4EyKxCmL4AYNB8BPjnIrrcZgLH0bh/RQztru3SY1HMbb5eKL4qCJCAzU0cCchggR5EeeJN23OYiBgS4YP/C+h6AiC636EM9TAmiNhjTUI/rg5gUnQ1BxDfH6qrzgHzrqM8Kt+7gra+RwAKip9BA8cAJI5EM8W+DiOLYARr9inQZJSXVOuqV0pT/4jEZg5E+67j9DX/yIyopmoDyIVQuiKE3AOv9leQOrgz+ElCMwlkOgq6WZeArKu8llUx/AUQOns7qrYipUelotMJ7fp2pNYoMDng912s6llXk9uHQcanl5F6OHv7Qn6q3PhzRgV5hQiVNqUwHv81N5zBg4v2xeJEBywNwHZm4ipxO+L8cXA7QlvdQXO5j/CgAGtExX268c6N2wCj7S+5zpH7w3//NnO6v6Kj+hOQHPr8xVVFQSfOBucs1seC4Yh0LJtKghOPR6c4z1v17Q9KycBt8bvCOyzD84t+2ScGDHeFjv2yaahNPuqCJ14P8747G05ZafWj9vkqyB06UuMPydqe6hSpLU47q39ymDPRXacWMzAuAlrsOUI+1nj6YAicMwx7Qta5HNhIF5NMBKxwxpSVUVUSpW/vFO7YjF44QW47z546CH4/nuoriZ4xP4EKmYQMc0EKgIEg0d3SvDkNQjMNZDIp6hDuQYpmjrXc3ntgVKqqEoxliPTyW269iQHVhmDJ2PsbO1vvAH//a+9zZuH8+GHOM2tEYzTty/HrrEut31/MAYfzb4A9XtOI7RBJcGDdKOVAAAgAElEQVQ9q3D2HoBTUUFDwnw/t3+0C1M+34WG09q/f20A7nrSZvdVVgpbO72pu6G1tyt+sh+XeMLf0qY8v49sqY+1tbb98edra7OvM3F8V+JcR16C7FCoNT0S7DlGcBdprRzhUaZy4cl/E4mfqZALA6XqlVVKFVdOqV3GwDvv2KBp2jR7DOnTBw48EI44AnbfHaeykoZODiJyDQI7KpDo6BLoSuVLAyhVMp09liPbyW2q9qQKLFp6XLb5EUdetvNrxG9ff9364qFDYYst7IFws83s3BvDhkF1NbUvC1Naijv4mPzcEJpnQeCutuXCQyEbQGQrLBAKtY6vGpdQrGDixLYly6uq0gcx2b6PVHNhxQs9xGKp2+c4tuR4roFZvC21tbm9Nhi0n7GpyfYY3nRTfn9j8WAmVbnwTMFmIRcGStErq5QqPk89Mp99ZoOm++6z1fMqKmzVvL//3Y5rShrTlEuAUowem3IZ31NOhRqUSqQBlCoL+Vbky+V1+Z7ctgQWCxcSvnAeNVfsaosOUEEDF9nUu002gT33hO23t3NubL459O2bsc0TJ9oiA4lV4ryW6E7Xxrq6tr0Y8TmR0k1C61VyT9PEiTZQiwdPmcaGFRIo5/raQns2499NdXXmcuGZ2pXv59UKe0p1fYnBy/gRSTnHP/5oU/MmT4YXX7SPjRhh5544+GBYa62ivH8xemzKZXxPuQRySiXTAEqVXL4V+fJ5XU4nt998Y7tPnn8ennsOPvqIEOcSYXdbmU2E0DH34Fz3CzseyYNUZcW33jp9kJTrSXWqgKsYPX3J6WXTpxc2Nqwj5ft5UwWJiVX0OoNW2FOq60oZvKy7PcyebYOm6dNhxQp7wa2uzs7VNHhwUdtQzB6bchjfUy6BnFLJNIBSJZfv2I9ijBlp04O11Up7oIvP4v7uu3ah/v3tHBsnnURwzX0JnOR3T7L9BI/fELzFTu3aHI3aOYh69cp8sp7LSXUxemBS9VYlB2YjR9pCB57GhnWSQucVS/57WrLElqpXSikv2gUvd56PM+VTmD/fXmQ76ig7CHW77Tpsnqbu2GNTDoGcUsk0gFIll+/Yj0LHjITDULNrjEgTBGQVDRV740Rm24hmp53gyCPtLO7bbNNShMABGjbK/0Q93uZ4cQJjin+yXkgPTDDYOl5q8mTb+RZfX3Jglm1eo86UqTfSa2ClY5CUUoUIrvs7AviJxKIEolGC9c/BxrvB5ZfDQQdB794d3gbtsVGqc2gApUqukLFJefW2vP8+PPIIoZtXJ9I4xqbjGT+h356Fc+G5sPPOGQ90hY7pybfCXC7y6Y0JhWwlv7hUlQqTCyaUOnCKS+w9amy02zdxHi8vaZ46Bkmp7qXTyl9/8AHceSfO3XfTUBUh9JvVCQ4fiTPnQlh//Y573zS0x0apjqcBlCoLhQy8z/o6Y2DuXHj4YXjkEfjf/wAIbnYcgW8MkZixcx9duz8ZJygqklwrzOUaDHkJGlKtMxhsW7GvK/XCBIN2TFk0ar/uyZNbt28uaZ7lFBQqpfKXazGFnIOtlSvtmKbbb4f//MdmKey/P84JJ+DssUfLPE3p1lvOcxsppbLTAEp1qHxO/ouyfHOznZDwkUfsbcECd1KgIIwdCwceiLPuujSkKM2dagxQoeNrUvFysp5PoYxsQUO6dcbLoRdasa8UHMcW5LjtNhtANTennrMpHhB2xPeplCofuRRTyCnYevdduPVWmDoVli61U1PU1cHRR8M663har85tpFTXpwGU6jC5nvzHx+DYCWGz9xa0W/+MJpyfn7U9TY89Bt99Z8cz7bEHXHop7LefrU+dIDGISTcGCPKrElgM+RTKyDaWJ9M6u3IPTG0tTJnSvgJhqnm8SvV9KqU6Ry7FFLIGW6tW2QtxN99sCw0FAvDHP8IJJ9gdi8+X03p1biOluj4NoFSHyfXkv76+NXiJRFrHsWRevyEaFSKNUUJ71uFELrZV8/bbzw7a3WuvtPMxpVpfqjFA8d8LqfaXr8RgyO+3c0aFw9nT0DKN5emuxRLSfe7koLAY1RtV+RIRP/A6sMgYs1+p26NKI5diCmmDrS+/hEmT7O2rr2DIEDvR7bHHepqzKd16u2OlPKV6Gg2gVIfpsBN1Nz+r+skv8UUPwyAETITg7pVwygxbOa+qKqdVhsM2OKmoaA2iEtvc2QFHYopZYtGJ22+3vSzZek2yTfTaXYsleOlB664BpGpxOvA+0L/UDVGl5bWYQptga/2dcT5pgjP/ZHudolHYe28bRO29d8vYJq/vnyqI00p5SnV9GkCpDpPriXptrQ0S4ie2tbUJTxoDL78M994LDz1E+JsNGEcDUXz4/MLEfwRwTv5bzm2Mj3mKV8Tz++HAA20qe+IYoM4MOFKlmA0ebNtXrF6TrpyqV6juHED2dCKyHrAvcDlwZombo7oQZ40tcR5/E04abcc5rbEGjBsHJ51kxznlu940QZxWylOqa9MASnWoXCeBff75pBPb99+3QdN998Fnn9kxTfvvTyhwIZH7exOLCgIsWZY6Bz2TeKASn5Mpbrvt2s/J1JkBR6oUs47oNenJhRR6cgDZzU0EzgH6pVtAREYDowEGDx7cSc1SZWvRIrjxRrjtNsJ9lxL63UCCY8/Dqf0b9OlT6tYppcqUBlCqrDgOOIMXEb5yNnUjlxD86j4QH6Fh5xO8YB2cv4yAfv0IhiHwcGEBRTxQiQdPIuWR0pUqWCp2r4kWUlDdjYjsB3xjjJkrIsF0yxljJgGTAIYPH27SLae6ubffhmuvhfvvh2iU8BE7UfN/YSLmawJLrqdhyX44fXSnqJRKTQMo1anS9nosW2bn1Lj3XsLPraSGZ4kQwO8/CfH7af5MCFwDDXu39h4UGlAkF2g49tjyKN3ttRhCIbSQguqGfgccICL7AL2A/iIy1RhzZInbpcqFMfD00zZwamiA1VaDE0+EceMILXqAyPNztDKeUsoTDaBUXpIDIS/pYO16PWZFcRqftwOQHn7Y5tING0Zo11uJhHoTjQmxGBCzx71sJbe9tiFxmVQlruvq8g/KipUW19EpZlpIQXU3xpjxwHgAtwfqbA2eup+8JqA1BmbMgAsvhDfegIED7Y5+zBg71gkIVmplPKWUd54DKC0Nq+KSA6E//hGmTbPHqF69UqeDhcMwYQI0NUEshi07vt81OMvOhQED4JhjbPfP9tsTfFkI1LT2DInYAgqZTvS9pKRlmkDW6zpy2S7lnBanhRSUUl1NzhPQGgOzZtnA6dVXYehQuPNOOPJIu5NOUC6V8fIKEJVSnS6XHigtDauAtulfjY22xkNcU1PbXqLWKneGVU2GGIKPqC07vsliOPMBOOAAG3m5kk/u4++Z6UTfS0patmUKTWvramlxWkhBdVfGmBAQKnEzVJF5noDWGHjuORs4vfSSLWN6++0wapSdpT2NUlfGyzlAVEqVjKcASkvDqkSJ6V/GtK1gF38eIPySoWbXGI1NgkEAHz6a2e3/vmDCNf1wDrg+7Xskn9wnBmSpgikvKWnZlik0rU3T4rq/nly5UKlS8zQB7X/+AxdcYH+uuy7ccosd4JrU41SOPAeISqmS89oDpaVhVYvEHqKlS+Gqq1qfO/tscDb5AW6oJ3R5jEjTqRj8gEHEUNXLz4QpG3geo5T8XLoUOS8padmWKTStTdPiureulKKpVHeUMc3upZdsj1NDg53I7x//gBNOaJPdUO48BYhKqbKQNYDS0rAqlcQeomHDYPp0w8htPmP04kth4P3Q2Ehws+MILINI1OD3S9Yqd9lOULOlyHlJScu2TKFpbZoW1311tRRNpcpRoWN82qXZvfqqDZxmzoS114brrrOV9Xr3LmKr0yvmmKVyGYellMrOSw+UloZVKYXDEHq2meDKpxj9zYVw5Vu2LOyoUTBmDM7WW9OQQ8pTthNUTZFTpaR/f0oVpqhjfN54Ay66CJ54AqqrbSrEySfbY1An6YgxS6Ueh6WU8iZrAKWlYctLuYzBCM/8keC+fYhEBT97cfPAuYy+ZQwcfjj0b60zkkuPTLYTVE2RU6Wkf39KFaYoY3y++grGj4cpU2wJ8ssvh1NPhX5pRxi0UcweIx2zpFTPpfNAdSFlMQZjwQKYOJH6f2xKJHocIETxcfLXF7HlbwSngBqNXscx6YmrKhX9+1MqfwWN8WlshIkTbcAUicBf/2oDqdVXB7wFRsXuMdIxS0r1XDkFUFoatrRKOgbjnXfgmmtaJ3wa+jR8HH9SiEbtPE8TJmSvmJeJnqAqpVT3lNcYH2Pg0UdthaJPP4U//MEeizbcsGWR8IIwu0zZpSWQeX7U8ynXXeweIx2zpFTPpT1QXUinj8EwxkZAV10FTz9tc8vHjoVx46j9cn1uH2GDubhnnoE5c2wvEnjrLSuXlESllFIdL6cxPvPmwbhxdk6nzTe3B5nddmu3WP3b9TRFmwBoijZR/3Z9yvdI7jGq7lNN3Zy6goIfHbOkVM+kAVQX0mljMJqbYfp0uPpqmDvXVja6/HJb2WjNNW1b1rcVYm+9tfVlxrT2jEH23rKySElUSilVXn7+2c7ldMMNNkXvpptgzBioKOyUJbHHqLpPNeOeHqeT1iql8uIrdQNUbhzHpn0XI9AIh6Guzv4ECIeaqDsgTHjwIXDoobB8OUyaBJ9/Dued1xI8xdXW2kqxPvevSMTeqqtbe8v8/vS9ZalSEpVSSvVgM2fCFlvA9dfbq3QffQSnnJIxeKr9TS0BfwBBCPgD1P6mNu2yziCH8SPGs2TFknbpfEop5ZX2QHVT2VLj2vb+GCYeGGLctB2IsC0B37001L2Cc86I1ugoheQJda+/3gZD48bZx7P1lmlZaKWUUgB8+y2ccQbcey9ssgn85z8wYoSnlzqDHEKjQjmNRdICEEqpQmgA1Q15SY2zvT+GaFSIrIwyfVoTESqJUkFEKgiZnXE89E/Giz7U1UEsZm/x3iQvPWWjRtmfmSbYVUop1U0ZA1On2uDpxx/tpLjnnQdVVRlfllx1L9exSFoAQilVCA2guqGs1fqMISj/IRDbjggVBCTKyFPXY87t/rx7g3LtTUoO8mrTZ1wopZTqjj77zI6tnTULdtgB7rjDFovIoljlyLUAhFIqXzoGqhvKOP7o7bdh111xxgdpGHI8lx71EQ0vVDH6hi1oaBAuvTS/Yg7xdD6vr9fxT0op1UM1N8N119mxTi+9BDfeCC+84Cl4gtTlyJVSqjNpD1QXlm6cU8pqfd98A+efb6/wrbkm/POfOKNH4yQMzC10DqZcXq/jn5RSqgd66y04/nhb4XW//eDmm2HQoJxWoeOXlFKlpgFUF5VtnFNLMBOJwDX/sF1DK1bA6afbHPM11ihZ2+Pt65SS7EoppUpv5Uq4+GI7CW51NTzwAPzpT7Z0aw7iY58m7jWRJSuW6PglpVRJaADVRXkZ58Rjj9nZ2z/+GPbd1x64NtkEKI8JbAvt8VJKKVUekos6tPHcczB6NHzyCRx7rJ1jMGlaDK/vUYyxT0opVSgNoLqojClw8+bZikYNDbDppvD007Dnni1P6wS2SimliiVtYPP99/Yi3uTJMGyYPdjsumvadWSriJdq7JMGUEqpUtAAqkyk6hHK1EuUMgXu229tet6kSTBggB2YO2YMVFa2eW3W3iullFLKo/aBzfM4L30Bp50GS5bAuefaY1Pv3ilf77VnScc+KaXKhQZQZSBVjxBk7yVqM87pupvgkksIL9+C0PaPErxoZ5w9+6d8Py3goJRSqliCQ4JU+CqIRWNUiJ/qO++n7pN5BH+9Mc7VM2GrrTK+3mvPks7dpJQqFxpAlYH6emhstMOWEkt6p+olmjQJpk+HkSNh9AkGnnwSzjoLPvyQ8A5nUPPWNURe9RE4KH1qnhZwUEp1NyIyCKgH1gFiwCRjzA2lbVXPYTCAIRqJcNqgeTQPFQIVX9BQvZJsh5hcepZ07ialVDnQAKrEwmG46y4bPAFUVLT2CCX3Ek2aZDPyAGbNMjBxIqPfP9MWhpgxg9BbexN5LXNqXmJa4PjxnfIRlVKqMzQDZxlj3hCRfsBcEXnGGPNeqRvW3YVeeYDoqgjGB1EfRH2CwXgep6Q9S0qprkYDqBILhWzAA7aa6zHHtAY9yb1EEyYAGMCWfZ3+0a8ZfcMNcNJJUFlJcEDm1DwtHqGU6q6MMV8BX7m/LxeR94F1AQ2gOkpjI1xxBcF7biJwBER8PvyVFQhCc6w5p3FK2rOklOpKNIAqseTxSLW1aXqJVq1i5IDZzKIGG0TByKu2h9NqWtaVLTVPi0copXoCERkCbA28kuK50cBogMGDB3dqu7qVZ56Bk0+Gjz/GOfJIGg45jNDSt1sCJu1NUkp1Z1kDKM0r71jJQQ+k6CX6YQaceSajP/gANr2e6Wscx8hR/Rg9um/K9aULirR4hFKquxORvsB0YJwx5sfk540xk4BJAMOHDzed3Lyub/FiOPNMmDYNNtwQZs2C3XfHARz2aVlMAyelVHfmpQdK88o7WGLQc9JJiQUlDKFRd+N8dCxstBE88QSj99mH0TnO3J74Plo8QinVXYlIJTZ4utcY83Cp29OtRKNw661w3nn2IHXRRbY8ea9epW6ZUkp1uqwBVE/MK880/1JHv68tKGEvilZEmwh+NQ2uv96mSgQCBb9Hph4qpZTqqkREgDuB940x15W6Pd3K3Llw4onw+uuw227wz3/ai3pKKdVD5TQGqifklXd2oYXEYC30bDPRVT7AhxDlmM1exZl9H6y1Vsc1QCmluoffAUcB80TkLfex84wxM0rYpi4nvCDcOn6papjtaZo0CX7xC7jvPjj0UFvxSCmlejDPAVRPySvvzEILrcGaIeCPMrHfBQTMBUQIEOjlo/aOnUBjJ6WUysoY8wLxEqUqL+EFYWrqa+x8TMZHw7RKnI+b4JRT4JJLYMCAUjdRKaXKgqcAqifllXdmoYVQCCJNhmhMiEQNSyrWoWHifwmt2E7HKCmllOpUofnPE2luIkqMSDRKaMQwnEcfsXMNKqWUauGlCl+PyivvtEILn3xC8Jm7CMT+RoRKApVC8F9jcX7vzzpru1JKKVVUL71E8Mr7CWwdI+KHQGUVwQvvgkEaPCmlVDIvPVA9Lq+8mIUW4mOcqqthyRIIbvYNzowL4K67cAIBGo7bgNB6RxLcs0p7nJRSSnUeY+wVw8svh1AI55e/pGGfcwlt2Zfg0F21FLlSSqXhpQqf5pXnKT7GqakJYjGDYKikPyH/ezgnjoHzzsMZOFB7nJRSSnW4lgIR6++M8/YSuOwyePVVGDgQrrsOTjgBp29fPSYppVQWOVXh6+oKLU+e+HrIvq74GKdYzMafBh8Rqqg/5Emcm/rn3gCllFIqDy0FIpqbCDQbGu42OJVD7dxORx8NVVWlbqJSSnUZ3SaAyhYcFVqePPH1fr+t4trcnGZdxsDzzxN8YgaB2CU0UoXB5z4p0F+DJ6WUUp0kEiE0/RoikZVEfRDxQegvf8I5+T6o6DanAUop1Wl82Rcpf/Hg5oIL7M9wuP0yqcqTZ1tnXV3ruhJfv2pVmnU1NcGUKbDVVlBTg/PxPTQcN40xtU1UVgoi9iJfbW3m91JKKaUKtmiRLT++wQYEr32YQEzw4yMQ6E3woDM0eFJKqTx1i72nl7mbcilPnqq3KvH1yT1Qwa2WwmU32dnZFy+GLbaAO++Eww/H6dULB6g9sW0PWWJxiXHjWt9r4kS32ESw8EIWhaYsKqWU6mKammDGDKivh8cftwfGPffEOf12GjZfndDns+0kuVogQiml8tYtAigvwVEu5clTBWTjx7d9PUDooW8IfjoZ548ToLER9toLzjwTdtut3UztiZX9EgM0EYjF7K2pCcaOtb/nk2aYqNCURaWUUl2EMfDSSzB1KjzwAPzwA6y9Npx1FoweDcOGAeAAzuAdS9tWpZTqBrpFAOU4tudm+nQYOTJ9oJCqPHmqXprqavD57DEpHpC1LPfb5TjzpkF9Pc6LL7bm5I0bB5tt5qm9iQGaz9faoyViH4vF0vekeeWlV04ppVQX1dxsg6ZHHoFHH4X586F3bzjoIDjqKHshT1P0lFKqQ3SLvWs43JoGN2cObLmlt2AhVS8N2HXFg5uJE4GffqJmv15EIkIAPw1Mxtl0mR24dNxx8Itf5NTe5B6zeNpecjpfpjTDXN+jkHUppZQqAytXwjPP2IDp8cfhu+/sDn733WHCBPjjH6Ffv1K3Uimlur1uEUDl29uSrrBEJGJ7gYQYS66tJ/Txx0RiE4jiJyJVhE56COemddul6WWT2NuVLp1wyy2LM24pl5RFpZRSZer77+GJJ2zQNHMmrFgBq68O++4LBx5oU8c9Bk0t80DpGCillCpItwigUvW2eCmg0PZ1huCAt+HllwnEjiaCn0BsFcEfH4NDdiHwsBBpNgQCfoJHrpfz1MKpervGj2+/XKo0w3wVc11KKaVyk3fAsnIl/PvftqrrM8/Yq3wDB9r5mg48EHbe2R5IcmhDdZ9qxj09jkg0QsAfoKG2QYMopZTKU5cOoNL16IC3AgrOoIU0jHuT+vsDsHAhnHwHjv81Gn79NqFfHkLwsF/hjJoOIjSEbVGjdO+fLVDRMUlKKdVztExc6zVgiReCmDIFHnwQli2DQYPg7LNtat7w4TavPM82iAgxEyNmYkSiEULzQxpAKaVUnrpsAJWpR6euLk2wYgy88QY89BA8+ST897/ADkyR54gQYEpgFA1PNOLs3pdUh5UpU+z6pkyx45YSxytlq3KnY5KUUqrnCM0PEYlGiJpo5oBl+XI7BcZdd8FHH0GfPrYa0qhRsMsuhBe9Qmh+A8FF0ZwDnsQ2+IwPv8+PIAT8Aar7VFM3p07T+ZRSKg9dNoDK1KPTLjXvVx/C+LvtVb1PP7WViXbaCa6+mtDCI4nc1ItoVIhEIfR6X5zds7/f9Om59SjpmCSllOo5gkOCBPyBlh6o4JBg2wUaGwnf+FdCDXcQfHcFzoZBOO88Gzy5Y5py7sXK0oaJe01kyYolms6nlFIF6hIBVKpUuUw9Oo4DEy/4jum3f8/I5VNwjrnCBk01NfC3v9kc8jXXtOsJQ2BS+/Ukv2fy+40caSv+5dKjpGOSlFKqZ3AGOTTUNqQeAzV7NuFzj6CmZhGRHSDwuyoajr6iXRDjuRcrxzbUzakraL1KKdXTlX0AlW5C2LQ9Ou+/T/ishxj31NlEGMAc3wVsOX4HnLN2tHXCk6RaT7r3TF6uWBXzlFJKdT/xoCQ0P2Tvr7cDXHstnHMOoQPWIFLpI0qMiGlOGcRk7cXy2IaOWK9SSvVkZR9AZUrVa9Ojs3Sp7V269VZC/vOJSBVR4yciFYT67Y/TPnZqkbiecNhOp9HU1H5C2+QeJO1RUkoplU67FLxFNTi3PAEHH0zwipMIPLhfxiAmYy9WATpqvUop1VOUfQDlqfjC3Lnwpz8R/nwgoe0fpfrgIIHz/TkXbIj3PMWDJxGb+acFH5RSqvyJyF7ADYAfuMMYc2Up29MmBS+yktC8J3DOPx8uvhjH5/MUxKTqQSqGjlqvUkr1BGUfQGUsvmAM3HYbnH464dX3oqbyYZpe8eN7Dc48EwYMSJ1el678eLy3KxZrXb0xHfjhlFJKFYWI+IF/ArsDC4HXROQxY8x7pWpTS6rcqpUEohA86gIYfUnL8+1S/DSgUUqpLsFTAFXqq3opU+WMgdNPhxtvhD33JDT8Pprq/MRiNgC6/nqYPTt18JRujqh4b1djY2vgFI3qnE1KKdUFbAd8bIz5FEBE7gf+AJQsgHLW24GGr/Yg9Pa/CR5yDk5C8ASFV9lTSilVGlln5Uu4qrc3sBlwmIhs1tENyyT8kqFupxmEb3wNzjgDZswguO9qbeYYjAc+yVKNqYqL93aNGQNVVeD365xNSinVRawLLEi4v9B9rHTuuQfnn/9m/O/PxRn793ZPp6qyp5RSqvx56YEqq6t64TDUBJuJrNqTQMXuNBxcieMTHMfORTh2rA2OqqpSBz7ZxlTFe7tqa7XCnlJKdSGS4rF2SdgiMhoYDTB48OCOa81nn9kD0k47wWWXpVwkVTW88IKwFndQSqky5yWASnVVb/vkhTrroBS642Miq4YQpYKIMYRmC86O9rnRo7OXFvc6oa1W2FNKqS5lITAo4f56wJfJCxljJgGTAIYPH95xo1xPOslWIqqvt+kMKSRXwwM0pU8ppboALwGUp6t6nXJQ+u47gtNPJSAPE/H5CQQkZQ8StKbmpQuiNDhSSqlu5TXg/0RkKLAIOBQ4vCQtmTULZs6E666D9dfPuGhiNTyd4FYppboGLwGUp6t6neKvf8X5+Vka6r8ktGBY2gp76YpEKKWU6p6MMc0iMhaYiS14dJcx5t1Ob0g0Cn/5CwwdCiefnNNLdYJbpZTqGrwEUOVxVe/FF+Guu+Ccc3COHEa6mCjTxLtKKaW6L2PMDGBGSRtRXw/vvAMPPGAH4+ZAJ7hVSqmuIWsAVRZX9VatsvnkgwYR3uMiQnXpxy95mnhXKaWUKrbmZrjoIth+e/jTn/JahU5wq5RS5c/TPFAlv6p3330wbx7hK56nZv8+GdPzvBaJUEoppYrq2WdhwQKYONEWkFBKKdUteQqgSu7mm2HTTQmZnT2l52mRCKWUUp2uvh7WXBP23bfULVFKKdWBsk6kW3JvvAGvvgonnkhwFyEQ0AlulVJKlZlly+CRR+DQQ3Me+6SUUqprKf8eqP9n787jo6rOx49/nkwyYRFEg4KILK51wRWV6zqK+hN3695q0Fpxr7RaFVssta34tWqpWhcqInHBDXdxjYxFHVRwwSp1V0ARFUQUSSaZOb8/zp1kMpnlzmSSWfK8X6+8SDIzd869E+65zz3Pec7NNxOqDhD89gwCpE7PC4U0bU8ppVSBPPAANDTA2LGFbolSSqlOVtwB1PffE7rzI0XS2akAACAASURBVEY3P0P4Sj/+a2xqeSItXa6UUqqg6upgq61g110L3RKllFKdrLhT+O68k2DjKMKmikgEGhvhvPNg4kQbMIVC9mnJSpcrpZRSXeKTTwh9OpfJJw8ltHReoVujlFKqkxVvAGUM3HwzgZ8tx18t+HxQUWGDpMRAKVa6XOdGKaWU6mqhur8xeixMjNYzum40oSWhQjdJKaVUJyreFL65c+G993CmXUj91jZYqqmB8ePbr/GkpcuVUkoVhDEE33iY8M4QMRHCkTDBz4K6lpNSSpWxogygQiEInv8lgXUOxDnxRJxerUHRiBHJAyUtXa6UUqrLvfQSgTe/wz/ST5gIfp+fwLBAoVullFKqExVdABUKwej9DeGGY/FX/pz6t/0aKCmllCpOM2bgfNeb+hMfJbj8NQLDAjr6pJRSZa7oAihbEMIQoZKwMSkXy1VKKaUKKhyGWbPg2GNxthyNs+XoQrdIKaVUFyi6ACoQAH91BeGwwe8XLQihlFKqOPn9sHAhNDUVuiVKKaW6UNEFUK0FIUQLQiillCpum2xS6BYopZTqYkUXQIHOc1JKKaWUUkoVp+JdB0oppZRSSimliowGUEoppZRSSinlkQZQSimllFJKKeWRGGPyv1GRb4DPO7CJ/sC3eWpOMdD9KW7ltj9Qfvuk+1Pcst2focaYDTqrMV5pX9VOue0PlN8+6f4UN92f4pbL/iTtrzolgOooEZlvjBlZ6Hbki+5PcSu3/YHy2yfdn+JWbvvjVbntd7ntD5TfPun+FDfdn+KWz/3RFD6llFJKKaWU8kgDKKWUUkoppZTyqFgDqKmFbkCe6f4Ut3LbHyi/fdL9KW7ltj9eldt+l9v+QPntk+5PcdP9KW5525+inAOllFJKKaWUUsWoWEeglFJKKaWUUqroFF0AJSIHi8j7IvKRiFxa6PZ0hIjcLiJfi8h/C92WfBCRTURkjogsEpF3ReSCQrepI0Skh4i8JiJvu/vz50K3KR9ExCcib4rIE4VuS0eJyGci8o6IvCUi8wvdno4SkX4i8qCI/M/9f+QUuk25EpGt3M8l9rVaRMYXul1dRfuq4qV9VWnQvqp4aV/lYbvFlMInIj7gA+BAYCnwOnCSMea9gjYsRyKyD/AjUGeM2a7Q7ekoEdkI2MgY84aI9AEWAEeV8OcjQG9jzI8iUgW8BFxgjJlX4KZ1iIj8DhgJ9DXGHFbo9nSEiHwGjDTGlMU6FCIyA5hrjLlNRPxAL2PMqkK3q6Pcc/cXwO7GmI6sq1QStK8qbtpXlQbtq4qX9lWZFdsI1G7AR8aYT4wxYeBe4MgCtylnxpj/ACsL3Y58McYsM8a84X7/A7AI2LiwrcqdsX50f6xyv4rnjkIORGQwcChwW6HbotoSkb7APsA0AGNMuBw6JNdo4OPuEDy5tK8qYtpXFT/tq4qX9lXeFFsAtTGwJO7npZTwSa+cicgwYCfg1cK2pGPcFIK3gK+B54wxJb0/wBTgYiBa6IbkiQGeFZEFIjKu0I3poE2Bb4DpbtrKbSLSu9CNypMTgZmFbkQX0r6qRGhfVbS0rype2ld5UGwBlCT5XUnfZSlHIrIOMAsYb4xZXej2dIQxJmKM2REYDOwmIiWbviIihwFfG2MWFLotebSnMWZnYAxwrptqVKoqgZ2Bm40xOwFrgJKeOwPgpnccATxQ6LZ0Ie2rSoD2VcVJ+6qip32VB8UWQC0FNon7eTDwZYHaopJw869nAXcbYx4qdHvyxR2eDgIHF7gpHbEncISbi30vsL+I3FXYJnWMMeZL99+vgYexqVOlaimwNO7O8YPYTqrUjQHeMMYsL3RDupD2VUVO+6qipn1VcdO+yoNiC6BeB7YQkeFupHgi8FiB26Rc7kTWacAiY8x1hW5PR4nIBiLSz/2+J3AA8L/Ctip3xpgJxpjBxphh2P87LxhjTi5ws3ImIr3dCeC46QMHASVbJcwY8xWwRES2cn81GijJSe0JTqJ7pe+B9lVFTfuq4qZ9VXHTvsqbynxtKB+MMc0ich7wDOADbjfGvFvgZuVMRGYCAaC/iCwF/mSMmVbYVnXInsApwDtuLjbAZcaY2QVsU0dsBMxwq7JUAPcbY0q+nGoZGQA8bK+FqATuMcY8Xdgmddj5wN3uRfcnwGkFbk+HiEgvbCW6Mwvdlq6kfVXR075KdSXtq4pcZ/RVRVXGXCmllFJKKaWKWbGl8CmllFJKKaVU0dIASimllFJKKaU80gBKKaWUUkoppTzSAEoppZRSSimlPNIASimllFJKKaU80gBKKaWUUkoppTzSAEoppZRSSimlPNIASimllFJKKaU80gBKKaWUUkoppTzSAEoppZRSSimlPNIASimllFJKKaU80gBKKaWUUkoppTzSAEq1EBGfiPwoIkPy+dxSJCIHiMhnXfA+fxWRO/K4vfNE5Gv3s1k3X9tVSqmuICLDRMSISKX781MiMtbLc3N4r8tE5LaOtLdYdfTYZPE+p4rIS3nc3tEissTtw3bK13aVyjcNoEqYe4KJfUVFZG3cz7/MdnvGmIgxZh1jzOJ8Pre7E5G7RCTsfi4rReRZEdkyh+0sFZFAmsd7ANcA+7mfzfcdaHbJEpELRGSBe8zbXRyJyDgR+dj9PGaLyEaFaKdS5UhEnhGRK5L8/kgR+SrbC3pjzBhjzIw8tCsgIksTtn2lMebXHd12uRORSSLS5J4zV4nIKyLi5LCdoIhkOt7XAOe5fdibubW4tInI8e4x/klEgkkeP1xE/ut+Hq+IyDYFaGa3pwFUCXNPMOsYY9YBFgOHx/3u7sTnd/adKJXWle7ntAmwEri9E95jIFBtjHk32xeKSIWIFPX5IIs2fgFcAdyRZBuj3ccOA2qApcBdeWymUt3dHcApIiIJvz8FuNsY09z1TVJ5cJ/bh20AvAQ8lOQzzoehQNZ9GNjMmDy3Je88tnElMAW4KsnrtwDuBs4C+gGPA4/p9V3XK+oLJtUxbnrYfSIyU0R+AE4WEUdE5rl3kZaJyPUiUuU+v9Id8h/m/nyX+/hTIvKDiIREZHi2z3UfHyMiH4jI9yJyg4i8LCKnpmj3KBF5Q0RWi8hyEfm7+/sKEXnQvYu5yr2btXXc6+4SkRvdO6A/ish/RGSA+36rRGSRiOwQ9/ylInKJ+/vvRGSaiFSnaNNgEXlYRL4RkU9F5NxcPhNjzBpgJrBdivc5SkTeddv7gohs5f5+JjAIeMrdt98lvG5r3E7HffxZ9/u9RGS+e9xfE5Hd417zkoj8RURCwBqgXTpmpmMkImeJyEciskJEHhF3NEdE/iYi/3C/rxaRBhG50v15Hffndd2f94z7m3xLRPbJpo1JjvGDxphHsZ1QosOxFwKLjDGNwF+B/UVkaKbtKqU8eQRYH9g79gsRWQ9706LO/flQEXnTPccvEZFJqTYmcaMWYlPHrxGRb0XkE+DQhOee5p6rfhCRT0TkTPf3vYGngEHSmqUxSOzIyl1xrz8i7vyb2L98JiIXichC93x6n9hR/2Rt3lxEXnSf962I3Bf32D/dfV4tdqQ8/jhNEpEH3L7sBxF5R0S2FJEJYlOzl4jIQQnHZrJ7bv9eRB4VkfVTtGld9/y9TES+EHt9kHXAYYxpAmZgb9jVJHmfPUTkdbc9r4vIHu7v/4b9m7jRPf43JryuWkR+BHzA2yLysfv7rd39XOV+NkfEveYOEblZbCbBGmC/JO1Je4xSfebu39Ljcc/7SETuj/t5iYjs6H7/MxF5TmyGyfsicnw2bUxyjJ83xtwPfJnk4f8HzDXGvOTejPg/YGNg30zbVfmlAVT5Oxq4B1gXuA9oBi4A+gN7AgcDZ6Z5/S+AidgOcTHwl2yfKyIbAvcDv3ff91NgtzTbuQH4uzGmL7A58GDcY08AW2BP3v8F7kx47fHApe77GGAeEMKe6B/FpgfE+yVwoLvNbYEJiY1xO5kngNexJ6oDgd+LHc1ARPYVkW/T7E/8tvpgj1O71AT3xH0XcD72Lt/zwOMiUmWMOQl7Mh3jjjBeF/9aY8wiYAf3+3WMMQeJSH/gSeBad/+vB2a7FzMxpwC/AvpiR2OSSXqM3I78CuBY97h8ib0zBvAiEHC/3x07KhQ7we8BvGuM+V5ENgEeA/6E/bu5FHtnM75jbtNGEfmDiDySoq2ZiPsV/zOkCGiVUtkxxqzFnu9r4359PPA/Y8zb7s9r3Mf7YYOgs0XkKA+bPwMbiO0EjMSee+J97T7eFzgN+IeI7OzeuBoDfBmXpdHm4lRsWvVMYDz2/Dsbe/71J+zHwcBwYHvg1BTt/AvwLLAeMBjbp8W8DuyIPd/dAzyQEIgdju3X1sP2E89gr9U2xp5vb014r1rs+XEQtn+/PkWbZriPb449fgcBscB0iBtAeJn/XI3d76XGmG8THlsf2+dcj+1zrgOeFJEaY8wfgLm0puedF/9aY0yjO8IFsIMxZjOxN3cfxx7LDbF9493i3lh0/QL4G9AHOzKWTNJjlOEzfxHYW+yN242AKuw1EyKyKbAOsFBscP4c9rPcEDgJuElEtk3VRhH5hYgsTNHWTJL1YYL2YV1OA6jy95Ix5nFjTNQYs9YY87ox5lVjTLMx5hNgKunvXDxojJnv3nW6G3viz/a5hwFvGWMedR/7B5Au4GgCtnBPuj8YY14FcPfhDvd3DcAkYBf3BBYzyxjzpvv4I8CPxph7jDERbACZOCn1emNMrCO4EnvySzQK6Ovmy4eNMR8B04AT3Xa9aIzpn2Z/AC4VkVXAB0A19mSe6ETgMWPMC+5xugp7IbB7kud6cTg2UJnpft53AYl3bW93R2Oa0qTWpDpGvwRuM8a85R7vS4F9RWQw8DKwjYj0A/bB/p0NF5Fe2L+3F91t1Lr7/Iz7+T4NvI29SEnaRmPM34wxXi62kpkNnCgi24lIT+BybKDdK8ftKaXamwEc5/4fA/v/vGUekzEmaIx5x/0/vxB7EevlDvrxwBRjzBJjzEpgcvyDxpgnjTEfG+tF7IX33sk2lMQJwJPGmOfc8+81QE/sDZ+Y640xX7rv/Tip+8MmbCraIGNMgzGm5cLeGHOXMWaFey67FtsfxAcEc93zYTPwAPbC/iq3TfcCw9zzasydxpj/ukHiROD4xJElERmADSDHG2PWGGO+xvbDsT5ssTGmn0k/p/l4tw9bAuwCJDsHHwp8aIy5092/mcD/sH1RLkZhA5Wr3L73BezNzPh++lFjzMvu31JDiu2kOkYpP3P3+ugH7Ge8LzaQ/UJEfub+PNcYE8Ve33xmjJnu7vMbwCzaBvdt2uhek2yf4zF5DtvPBtxA7zLAj/ZhXU4DqPK3JP4Hd6j5SbFpcKuxd7TSXfx/Fff9T9iTWbbPHRTfDmOMIfVoB9g7h9sA77vD7oe4bfeJyNViUzNWAx+5z49v//K479cm+Tmx/fHH53O3rYmGArE7dKvcTuRi7CiYV1e5HdRGxpijjDGfJnnOILcNgA0Yscdp4yzeJ+X2XJ8nbG8JmaU6RontXQ18B2zsdlRvYoOnfYAgdjTQoW0ANRQ4KeHYjqLt5+CljZ4YY57Bpu09AnwGvI/9u0j396iUyoIbMHwDHOnerd8Ve4ceABHZXUTmiE2J/h47nyPTTShI6EtIOL+JTRWf56ZSrQIO8bjd2LYTz79LaHu+9NofXowdFXjNTQ9ruWEmIheKTTP83m3juqTvw751bwDGfibhfROPRxXt93mo+/tlcefZW7EjJl7d7/ZhGxpj9jfGLEjyHC99TjYGAUvczyLV9nLpw2LHKNNnHsuk2Mf9PojtvxL7sN0T+rBf0vb6IJ992P+AscCNwDJ3P95D+7AupwFU+TMJP9+KTX3b3NgUuctpOxzcGZZh0xgAEBEhzQnVGPO+MeZE7Mn9WmCWm+JQi+0Q98d2OpvHNtmBtm0S9/0QkuccL8HeVesX99XHGJPrXbVUvsSejAE75wt73L5wf5X4WWa1PdeQuO153WaqY5TY3j7YtJPY9l8EDgBGAAvcn8dg717OdZ+zBJiecGx7G2P+nmUbPTPGXG+M2dwYMwB7NzOK7YCUUvlThz1nnwI8a4yJDwzuwabubmKMWRe4BW/n8WW0Px8BLalls7CjCAOMMf2wI86x7WY6jySez8R9ry9SviIFY8xXxpgzjDGDsCnyN4mdF7U3cAl2JG09t43fk98+rIn2GR5LgEagf9x5tq8xZlvyK1Ofk0sftom0LR6Ujz4sdowyfeaxAGpv9/sXaR9ALQFeTOjD1jHGnJ1lGz0zdp7vdsaYGmz6+1BsaqjqQhpAdT99sCfsNe6cm3Tzn/LlCWBnsaU3K7FzsDZI9WQROUVE+rt3g77Hnnyi2LY3Aiuww9V/y0PbzhORjd05NxOwaX6JQkDYvXPYwx0JGyEiu+Th/ePdDxzhDs1XYeeM/QC86j6+HNg0i+09AWwrIieILfrxC2zQOTvLdqU6RjOB00Vke/fiZTI2rSF2J+xFbK78QjcdJQiMAz5wU2DA5vofLSIHuse1h4jsJyLJRgI9cfe1B3YycmybPvexniKyrVhDsTcU/mG6acl3pTpRHfYGyhnEpe+5+gArjTENIrIbdo6IF/cDvxFb1Gc9bNpwjB+bDvcN0CwiY7DzfGKWAzWSen28+4FDRWS0e/69ENvfvOKxbS1E5Dg3lRnsqLwBItj9bnbbWCkil2PTtDviZBHZxk2PvgKbSh+Jf4IxZhk2nfFaEenrzuvZTETyXXhgNrClO8enUkROwGaTPOE+nm0f9ip2vtzFIlIldhmPw7GpjNlIdYwyfeYvYos+9HT7tbnY9PIaWucxP+Hu8yluG6tEZFeJK0CSrVhfCFQCFW4fVhX3+C7uczbA9mGPuyNTqgtpANX9XIgd/v0B+x8vWcCQV+6dxxOwE0pXAJthTz6NKV5yCLBIbOXAa4ATjDFhYDr2jtGX2IpzWXdsSczEFmv4GJvOdWWS9je7bdoNm/b1LfbY9YWW9UVWdbQhxpYfHwvcjO1gDwaOcHOzcdv2ZzdNYLyH7X0DHIG947kC+C1wWFzw4lXSY2TsfKUrgIexd4aHYFMXYl7CBrr/cX9eiL3zF/sZY8xn2EInE919Xoz9G015bhKRiRJXHSmJSdhUl4uwAdxaWouD9MR2vj9iUwpfBP6cZltKqRy4/7dfAXpjR5vinQNc4Z7jL8deyHrxb+xclLeBN4CH4t7vB+A37ra+wwZlj8U9/j/suewT9xza5iaNMeZ94GRswYdvsRfqh7t9T7Z2BV4VW1XuMeACN237GWw1wA+wqWMNdDy9605s6fivgB7YY5BMLTbIfA97fB4EYlVTh4itjJexiEQ6xpgV2DlBF2L7nIuxfU5sROyfwLFiK7qmKnYRv70wtg8bg/1MbgJqcwgWkh6jTJ+5MeYDbF8x1/15NXYe8cuxINX9uzsIO5/sS/c9/g8bzCclIr8UkXSl2k/B9ls3Y0e/1mL/9mP+CazC9sersDcpVBcTOx1Fqa7jjgZ8CRxrjJmb6fmd2I6lwMnGmGCh2lDs9BgppVRxErvI6l3GmHYLhitLj5HqLDoCpbqEiBwsdh2KauxoQzPwWoGbpZRSSimlVFY0gFJdZS/s0Pe32NS0o4xdyFQppZRSSqmSoSl8SimllFJKKeWRjkAppZRSSimllEeVnbHR/v37m2HDhnXGppVSSpW4BQsWfGuMSbmUQa5E5LfAr7Flo98BTjPGNKR6vvZVSiml0knVX3VKADVs2DDmz5/fGZtWSilV4kTk807Y5sbY8sTbGGPWisj92NLCd6R6jfZVSiml0knVX2kKn1JKqXJRCfR0F+zuhV0uQSmllMorDaCUUkqVPGPMF9iFtxdjF3b+3hjzbGFbpZRSqhxpAKWUUt1QKASTJ9t/y4GIrAccCQwHBgG9ReTkJM8bJyLzRWT+N99809XNVEopVQY8BVAi8lsReVdE/isiM0WkR2c3TCmlVOcIhWD0aJg40f5bJkHUAcCnxphvjDFNwEPAHolPMsZMNcaMNMaM3GCDvNexUEoplU+vvw7LlhW6Fe1kDKDiJuaONMZsB/iwE3OVUkqVoGAQwmGIROy/wWChW5QXi4FRItJLRAQYDSwqcJuUUkrl6o47YPfdYb/9YM2aQremDa8pfDoxVymlykQgAH4/+Hz230Cg0C3qOGPMq8CDwBvYEuYVwNSCNkoppVRubrkFTjsNdt4ZPvgALrig0C1qI2MA5XViruaVK6VUaXAcqK+Hv/zF/us4hW5Rfhhj/mSM+ZkxZjtjzCnGmMZCt0kppVSWpkyBs8+Gww6Dl16CSy+FadPgvvsK3bIWYoxJ/wQ7MXcWcAKwCngAeNAYc1eq14wcOdLo2hpKKaWSEZEFxpiRhW6H9lVKKVVkHn0UjjoKjjkG7rnHpkk0NcE++8B778Fbb8Hw4V3WnFT9lZcUPk8Tc5VSSpWIpia45BK49dZCt0QppZSyVq2yI0877AAzZ9rgCaCqygZTAL/4he3DCsxLAKUTc5VSqlysXWvv7F19te2onn++0C1SSiml4OKLYflym65XVdX2seHDYepUmDcPJk0qSPPieZkDpRNzlVKqHKxeDWPGwBNPwLXXwtZb27t5X2pdIKWUUgU0Zw78+99w4YWwyy7Jn3PCCXD66XYRwxde6Nr2Jcg4ByoXmleulFJF5scfbbm9t9+Gujo46SRYtAhGjrSd1QsvQGVllzRF50AppZRq8dNPsP329vuFC6FXr9TPXbPG9lvff2+f279/u6eEloQIfhYkMCyAs0nHqiR1ZA6UUkqpUnfxxfDGG/DwwzZ4AjsCdeutMHeuXVVXKaXKQGhJiMlzJxNaUh6rhJe9SZPg44/tCFS64Amgd2+4915YscKWOU8YCAotCTG6bjQT50xkdN3oTvsb0ABKKaXK3fPPw803w+9+Z8vCxjv5ZBg3Dq66Cp58sjDtU0qpPOmqC2iVJwsW2JTyM86wC+Z6scMOcM01Nh39hhvaPBT8LEg4EiZiIoQjYYKfBfPfZjSAUkqpshIK2fTwUOyaYfVqmzO+1VZ24adk/vlP2HFHOOUU+Pzz1NtSSqki11UX0CoPmpps/zRggC1slI3zzoPDD4ff/96WNncFGIo/KvjEh9/nJzAskN82u7om4V0ppVSnC4Vg9GgIh2311/p6cKZfBEuXwssvQ8+eyV/Yowc88IBd8f2EE+A//yG0wN9+W2Wy4K5SqnwFhgXw+/yEI+FOvYBWefD3v9t5uQ8/DP36ZfdaEbj9djsadeKJMH8+3H47ziWXUL95b4JXjiOw49EdngOVigZQSilVJoJBG/BEIvbf4NQPcO74t13zadSo9C/efHPbGR13HFxyCcEN/9F2W0ENoJRSxc/ZxKG+tj5vRQRUJ3n/fbjiCjj2WLtwrkeJBSJCN15K8LrfEAhshrPgazj0UJxp03AGDOjExmsApVS3EwrZi+FAQC+Iy00gYEeL7KiRIfDERbDNNt7XzDj2WPjNb2DKFAJXHonfH2gZgaqpsel8+nejlCp2ziZOtw6c4oMMIGUwmc9qdVmJRODXv7YFI9w5TF7aEpvfFhtdnHLwFMa/dwnh0eBv/pr6Uy/GOfcqOzrVyTSAUqobSZri1X37mLLjOPYzDQYh8PKVOE/Phtkhm6KHx+D573+HefNwrjqS+hnvEvxoMDU1MH68Idxo8FdX6N+NUkoVqfggw1fhQxCao834fX7qa+tbgpPEYCTxsU4NrP72N3jpJbjjDhg4MG1b4iXOb5v13iz7s0DY7yO4Qz+cLgieQItIKNWttEvxCha6RSrfHAcmbP8kzpN/hEsvhV13BVqD54kT7b8pC0P4/XDffeDzwR//CM1NvPn0csJrI0SiFYTDRv9ulFKqSMUHGU2RppQFNVIV2+j0KoazZ9usiJNPhtratG2JtSdWkj42vy1WIOKYbY5p83NXznfTESilupG2KV72Z1VmvvvOloMdMaLN2k7JgueUo0jDhhGa8BijL96Z8OWCj3WpJAIVFfj9Ffp3o5RSRSq+iEb8CJSvwsfi7xcTWhLC2cRJWWwjWTCTt1GoBQvg+ONhp53glltaUu1StSXZyFTi/LYRG44oSBqiBlBKdSNtUrwCmoaVq2KbR9amPTf9Br75xq6PUV3d8pxsg+dg816EiRDBByKccWozQ7aoKJp9Vkop1V5iEQ2AurfrmP7WdP79xr+Z8faMlhS5ZMU2Oq2K4XPP2Sqv/fvbNQd7907Z5lhbkgVzE/ae0CZQKtR8Nw2glOpmHEcvgONlGwx11jyyXIOyNu2paKa+6SOcP/3BliSPk23wHAiAv6dN2fP7fdSe4dO/G6WUKgGJQUXwsyDN0eZ2o0rJgo+8VzE0Bq67Di6+2BY1evRRGDiw5eH4+VYT9p7Q5qXFXJJeAyilVLeVSzCUVSpcju2YMgVWrPAW6MS3pzECk9a/nkmjdyLZy7IJnm3AJUU10qaUUip72QYieRvV+fJLu9DtPffAMcfYohHrrNPycKbiEcVckl4DKKVKWLGlkpWaXIKhxFS4bMp7p/q82gRBjXaB9WjUW1AXCIC/KkpjJEoUH8+vGsnc/yd5GRnT0UqllCp9XRKIrFkDn38On31mv157zQZOkQj89a9w2WXtyot7mW9VrCXpNYBSqoM6knrVkeBn6lR7oR2J2KkuWlo6e7kU1YhPhbPlvb2NYKUb7Ypvhwg0N9ush8bGzEGds+op6qumMCk6geeb9yUalbyOjJVSgC4i/YDbgO0AA/zKGJPnElJKdR8FWycog2JtV1fK9hh4DkSMgU8/haVLbSpEqq+1a6GqCpqaYPFiO/c2Xu/eMG4cXHghDB+e9K1ySdErls9eAyilOiDX+TAdnUcTCsG559oLbfB2oa3ay7WoRmxkZvLkzCNYU6fCrFl2vcBUz41vx6pVcPXV9vfRqA3SkopG7SruV1yBM2IEk/64OXPHSt4qLJbopLfbSQAAIABJREFUmmH/BJ42xhwrIn6gV6EbpFSp8ro2j7Yrs3xd9Me2U9OrhvFPj8/fMVizBp5/Hh5/3JYZX7as9T0HQ3AYBL6swmnYwHZK668PAwbY4KmigtCowQQHrCUwaA+crQ+EYcPsPKeK9KslZTsyVkyfvQZQSnVArvNhElO2Jk2yX14vUINBe/0c4/NpSfJcdSRNLdMI1tSpcOaZrT9XVdl/kz03PiirqLCfb0WFvdHXzvz5Nh3iuedg7Fi46SacXr2oH5y/EaPOmOvVmUSkL7APcCqAMSYMhAvZJqVKWaeWsy6DdnkNivJ10R+/HREhaqJETTT3YxCN2jtjt91mCzs0NkLfvnDwwYT2HkZwnW+pWXcjfvPfvxOONuH3VTBn7IPt3qfN/n01l/qDDsIZNKjdc1Idq2xS9IrlswcNoJTqkFzXVYq9rrHRnsOefx7mzvV+lz8QsGl7jY32IvvGG7smfTDd6wqd7lWI9880gjVrVtufd9oJjjoqfRtjn204bPBXGQI178K979rc8sWL4Y03YN486NPHrqMxblxLXnk+5yyV4JphmwLfANNFZAdgAXCBMWZNYZulVGkq1gpoxdCubIKifF30x2+nwlS0rPGU9TH44guYPh2mTbNzldZf364deNRRsPfehJYvsPu20t5/ipgIAI2RRurermvX9kz7l89Ro2L47GM8BVCaV65UcpkuoFNd1MdeN2mSDZ6i0ezu8qd730yBRChkH2tqsiMiXt8zXUpXodO9Cvn+6YKWY46BZ5+N/WQ4fcRrjPO9CI+uhOkrYeVKO8S0cmXLl/PTT9T79iJo9iHQ8ALOmfNaN7jeerDppnDddYRGjCP4em8C8zpnX0twzbBKYGfgfGPMqyLyT+BSYGL8k0RkHDAOYMiQIV3eSKVKRTFUQEs2clEM7comKMrXRX/ids7f/XzeWvYWx2xzTOZj0NQETz0F//63TdGLRmH//W3Kw1FHQY8eSfct23YlLtabuL2OjhoVw2cf43UESvPKlUoh1QV0pot6x7EB1Ny5+bvL7yWQqKuzj4P99+qr4eGHM287XUpXodO96uqgocHOfY29f6xd6QLMVM/JVtKgtamJcZu+CKNXMCs0iGN+qmPctNvsY1VVrXnk669v88V33tl+37s3TlMTTgWwyVgYMhGGDoUhQ6BPH0Ihu7/TJ9g5cJ0ZMJZYFb6lwFJjzKvuzw9iA6g2jDFTgakAI0eONF3XPKW6Rj4n2ReyAlq6kYu8tssY+OEH+OorWwjhxx/t15o1bf+NFU3o2ZOAfzl+U0EYgx8fgcUCc+bYeT8DB0K/fq2ZAXm66I/fTvwcqLmL5zJiwxHJt7twIcyYAXfdBV9/bdt2ySVw+umw2WZA+7+XxIDIGENztJkqXxW1O9SmbFeqxXrzPWpULFX5MgZQmleuVPIL5EwjPV6Cilzv8scHSj4f/OpXUFubWyDz6KN2rs64cen3O11KV0fSvbyk3mVKHbz9dtsHAlRW2tgkWSCZeNxiFe86EoS0C1pv/QjnzZsITf8fwVU7EKh6hXGje8GYMRB4244g9e7drpxrNu8VCxahNOYndQVjzFciskREtjLGvA+MBt4rdLuU6krFNMm+ozo8ctHYCMuX28Ao09fatZm316OHHcmJRHDAzjkdBoHPwjhLJ7R9bt++NkBxv5zNNsPZbHeIDrIdtM+XzaFoEQseJs+dnPrYfPqp7djr6uDNN23Qd/jhdr7sIYfYTtKV6u8lPuADMgZ/ziZO2sV6i2XUKJ+8jEB5yivXtAhVrpKN6kDmkZ50QUViQJDtxW98oBSJwK232ptMU6ZkDmRqa+0ofsQdnTfGlkMfMaL9SE3iPqYK9rIJBBNHgNIdx5bRluktxX7417/aBnvBYOu+iMBpp9msuPhAsq7OPm/x4tbfx4pwGGMDkrq63IIQ+1kYIhEhvLaZYO008L3OaJ4nXFGFv1Kov9wGS8Encw8UW9+rNXgSKZn5SV3lfOBuN1PiE+C0ArdHqXY6swxz3ifZr14NH35o58188439Wr3anjTXrrVfDQ32uT6fPUn7fParutoGHdXV3r6P/50IgfBG+KWSsAG/VBL4vh88/bQdEYqNCq1aBd9+a0/6if+uWpV8n2pqWkeK9tij9fuBA2GDDWzw07u3XfA19m/Pnq0V5ZqaoKEBZ+1anPhj8N13NmD78ks7t+ijj+wI0KOP2tfEVFXZst5xAVbL1/Dh9r0yaDOqU1FF4MMwPHKhPT7vufeNdtkFbrgBTjqppZxr4t9eqr+XxFEeL39D6UaaimXUKJ+8BFCe8so1LUKVq/hgJXahPWSI99Glurq2v8/HfJ1YcBYbiYilrq1Y0T6QSRas3XQTnH12axARibTfh2SjWRMmpG6rl0Awcd/Hjk19HJONtkSj7YO9xEC11s0wiP3O57MBWHOz/T52883ns9ttarL/Tp9uX+t1PlnoP00Eb/uQmnf/gz9SS5gq/BIhMH5ngr3+SPiqaiLR1gBuxoz0n7mXv4v4fY0feezuo08xxpi3gJGFbodSiTqt/HSCnNOl1qyxC5+++iq8/74Nmj780KZ9JfL57EV+7Cs2fyYabb2r19xsT1SNjfYkHh9AeNR2lCeCs/Sc5E/s3dsGCP3723833dT+O2AADBxIaJ1VBPmUwBYH4YwYY0+iHVFVZb/69PH2/EjErqn08cftv15+2Qak8Tbe2AZTm25qUwFjx7iy0qYarliBs3Il9d8PIdj8MYGPGnCWTrL7tddetiDEYYfB5pu32Wyy0aZ8pteV60hTKl4CKE955Uplq9BV2xKlak8gYPuLSKT1Qvv6672nrMUunGfMaA1uYtX30q3flO74xAdnsdEZEdtnxAcyqVL9YiM48Qvx1tTY+aSx98s6La+hwd59S/gKvdWT4AeDCPR9g+CyrQivHUuESsJrm2HGXfgjx9vgI9pM4KaxcO/70KMHwa9PJ7z2dAw+bO0aACESMQQf/xFn1DogknL0K/a7xYvbjridcYYNgAMBe/xuvdV+rs3NyQO4NgHNKAOhEKFrXmb0w+cSZkv8DGfK/o+xYtt9CJy0EY5zHITAf13rayFzwN2ZKZ9KqcLJe/npNDxfxH77LbzwArzyir2If/PN1pPkRhvBFlvYtK8ttrBfQ4bY0Zn+/W3Akq1Yhxf7amhI/70x4PPh+Hw4sVGtykq7oF5sZKh3bxtgxBVASNQmaPh4OvUbFiCl0eez81iHDrWFG+IZY+98xgKqjz4itHQewZ/eI/DWezgfJwSgVVV2rmxNDc6gTXB2PhRO2wF23BF+9jPw+91g/QEC1W0//2SjTRP2npDXoKccR5pSyRhAaV656gyFrtqWbXt23BFef731QjvZSE8yyS6Ka2paR37aLJQayyVraCD0UoTRx69POCz4/Yb6+1bg7NrcmhZRXY0zqhrHEXbayY4mNTe3H51JlepXX2+DqBEjWts0fnzC/u+4lvqZPxCsjxDY8kucLz6FW1ckDZJYvrz9XTQgxCgCzKGJKqo4mhs2/Qf+imbCUfD7ItQetJxa/60EF29KoP9/cfr4oGFTQsuGsbhxQyqIEAUqiACCQaiOhglMPgimvG3THYYPx9l0U5xNN4Wvh8M7mxL6ejOCr/VqCfriR38SR2ziH4sPEtt8do1RguMfw1k+Hj7/nGDlRMJUE8FH2OdjxQEnMGFC699SMGjTKVesIGkbkgWjXgPWEivsoFS3l7fy0x6lvIh97z145BF44gm7FIIxdnRjt91sYYE994RRo+wFep6Fvni1ICMTxbRuUFIiNijt3x92390N+K6yAd/WcSOUkYgNotz0xkShJSGCr16bdoQz1WhTdwp68slrFT7NK1d5lUuxg3xINaqTqj2xwCp2Q6yiovXi1suFbOtFsbumj38ewcehglFEqUCI8ubv7obfngU//dTaHi4lzF/sSE1DM8Ejr8PhqvZvUFXFU9EHiUYPB4SmJsPVBz3Pw5tfDNXVBBp3ptJMIUIlIBjjztM5/R6cTe/HiUZxmpqY/MExhNf+2h0ZaiK4zxU4zX/FwaZRtLPeejY9YsAAu7hR7PuBA1u/HzCAur8MInybPc2E8fHmQZdSXxv7DCpxnEug5T2ObPmMYsc8iu0rKv0+rr82zIqPVxEY/BFO9cnw6Z7wySf26z//sakNwFR+zXn8iwgRqiuaqB89mfrjNiL44y4E9qvAGbg+rFwXevTA2a2a+ucrCD7XRGCH73B6L4fHP4d33iEwpwF/dAJhKvFHmwi8cwMcuANccQU1q46j4iIfJgJ+v7QEO+kC8UwBt+PYoGvWLFv+XIMkpcpD4oXrlIOnsOKnFS3BRGfOieKDD+C+++zXu+/a3+2yC1x+uS1ss/POrSt8d5KuKGyR6hgW07pBXqQM+GLzypLwOsLZ3VLsOpunAErzylW+daRqW67SXdymak8ssIpGbfB0wAG29Hjai1tjbO74vHk4oRD1g38i+NFgu6bPRfOAUVQyhzDVGITpa0+k9riVOD/7zqYi9OhBYNkw/P+AcCSK3ycEfjcKht9ih5ni0yAaG/ly+vawvPXtv6waCptsYh//Gowx2BQ4oYIIfmki0PQ8LFvWclIObPgu/qURwkbwVxgCx20II65sLbVdU9P6tcEGbXLI06ZiJjnDZAo844957HA2N8OK1X4mXLcBsAHtwjpjYOVKQo8s59wzt6I5UgEIjVEIzl+HCavPw4lE4CHs7aD49ojgmCTTNgcezdghc/iqeigDt+oHFz0N+1QRCsH40TbYrqiwQY+Xcu6Z9jsUah0FnDu3fVEPpVRpSnfh2inBxerVcO+9dqHU116zv9trL1tQ4Oc/h0GDOrb9LHX2KFCmcue5BA1eg9p8B7+5BHzZjHDqaFP+eB2BUiqvCjGXI9PFbbL2JAZWKYOnJUts9Zunn4YXX7S5WwB9+uDsvjvOcYNhq7Nh82txNtuMX/2pmlunig0OpIq69S4gWA2Bfd0LbaD+qFh6XQXBFUfC9snf+/Th8NqZsZ+E06/aEsY9Zvd5MjQvpHUKUYWPKTf3xBnXtrKFA9S3BEI+HCchykghU+pjba2do5VY4CGd2DGPzROLH/VLyZ0AFvy6hmhcLOSr8hF48vew62/tJN5PPrHVkX78sSVdkqYmm1MfW5Np0CBCa7Zn9BG9W9vwEcx4vvVvJBbgibR+1PFtz+XGQKFGZZVSnS/VhWveggtj4KWXbNB0//22Mtx228G118Lxx8PgwXnYi9wkBgU1vWqYPHdy3oKOTMcw26DBa1DbGcFvLgFfphFO1Tk0gFIF09VzOTJd3CZrT8pAzxibTz5rlv1auND+fvBgO/F2jz3sk7feOumwe+1YmFHXvkpcfBASe69Mc8ViBSFiqV8jRrQWgwgEbAASG82Bthf8mfY/XuJIUyhkA8pYkJHsot9x7NqC2QTK8ce8pqZ1HpGX1wYCNkW8sdHu9403xl5XaRerHTYs80awgWf8KFj8/qX7O+rIjYFCjMoqpQrL64hDypGOr76yEyxvv92m6/XpA6ecYhdK3XXXnNaby0W6kZhUC8DmK+jId5qe16C2s0bWsg34NDWvMDSAUkUh14p82bwu14vbNoHFxx8TunIOwcdXE/jmARx51U68/fvfbT75Nttk7LASiwzEV4lLDEK8jkqMG2e/ko0I/etfbavt5XJhnrjdKVNsupmXUaJcAuVcg+uOjmzGPpuamtSjYJneo1BtV0oVD6+pXV4uftuNdPziGZyFK23Q9OST9uS+995w2WVw7LG5VcnrAC8jMZ4WgM1RvgMIrwFZMc2v0tS8rqcBlCq4XCvy5fK6nC5uV6+GBx6AO+4g9FIzo6knTDX+qvOpn/U9zuH9PW8qWVnxnXZKPfKQ7ahEsoBrwoTWanu5XpgnbnfWrBzmhnWRXAOYZEHiihXJR8E6a/RUK+wpVfqyTe3KdPHbZqSjqYHgOWNwnl5ji/ZcdJHtSLbcslP2w0tQks1ITGcFHfkMILwGZDry071pAKUKLte5H/mYM5JyBCsSsRHZjBnw8MM2n3yrrQgedAvh+p5EIkI46iP43/44h3t/v/g2x8qK9+jRtuR1YgpcNqMSqQIurxfmoVDrwr/x5b4Tt3vMMbbQQca5YV2oo+uKJf49rVhBS2lypZTyKq+pXatXE1i4Gn+zIQz4o4ZAzS7w+O/h4INbVwbPs2yCwGyColyCjk6tUpiC14BMR366Lw2gVMHlOvejo3NGko5g9Vtkg6a77oIvvrCL9J16KowdC7vtRmCe4J+b+3vG2tzQYKdRGZP5Yj2bUYmOpIGFQvY14bD9efp0O38p9v6J2+3oqFY+pRuN9BpY6RwkpVQ+dHiUJRy2xYjuvhseeADnp5+o33sYwTFbEzj0XJztD+2UdsfLJgjMNijKJujoihLoSuVCAyhVcB2Zm9SROSNtRhwaowSPuxnni/Nsbt3BB8M//mELQsStct7R94y9vq6ubeGIfF6s55oGFgy2LnYOmctwF1O6Wfxn2dBgj2/8Ol5e0jx1DpJSKh9ySu36/nt46il49FGYPdumjvfpAyefDL/6Fc5uu+F4LAiRjxGbbIPAzhqJKfqFcFW3pQGUKgodmXif04VuYyMBQviNQ5gKu1Bqr9ds0HTSSXYh2Hy/Z8Lra2s772I906hLsscDAbueYmwEqpRGYQIBG/dGInZUb/r01uObTZpnMQWFSqnSEx+8TNg7Qw7wkiXw2GM2aIrdwdpwQzjuODjySDu5tGfPrN8/HyM2xTK/p5gKNSgVTwMo1amynZfSqc+PROwLZs6Ee+/FWbmS+vUPJbjtOQTO2BLnlBl2DtAk+/T4OUCdIZt5Sdkek3SjLqkedxz7PsnmQBU7x7HzqG+9tXXh3XQlxzs6X0oppRJlDF7Wrm09+cyeDQsW2N9vuaUta3rUUbD77kmXvvAqnyM2xTC/p1gCOaUSaQClOk22VfJic3CamuxISKbRAk/bX7UKnn0WHn/cpkesWGFT8o4+GmprcQ44AMedhJtuDlChLrhzqTSYadQl3eOlPAJTW2unr8UHS8nS8nKt+qiUUum0C17eeRzntS/tSeeVV2zAFA7b0qW77w5XXWVHmn72s7y1oRxHbIohkFMqkQZQqtNkmz5VV9cavITDrfNYst7+Bx/AE0/Yr7lz7XBETQ0ccggcdhj8v/8H666bdHvJ5gBB4S64c6k0mKkYQrkWS0g1hykxKMxH9UallGojEiEQ3gi/qSBsovibowTOnAxLsQvwjRwJF1xgT0577QV9+3ZKM3TERqmuoQGU6jSdfaHeun2D3xch8Mb1sOUt8OGH9gkjRsDvf2+DpgxpEaGQXdC2srI1iIq1uRAX3IkLusbWjVq82D6WaR5PpoVey7VYgpcRtHINIJVSbXVq+euGBpg3D156yX698grODz9QPxiCI/oQ6D8S53eH2xPSTjvZIKqL6IiNUp1PjDF53+jIkSPN/Pnz875dVXqySX0LhWC//VovbGPpc22Ew7BwIcybR+iJFdS9sjn8sJpa6nB6vg377msr5x16KAwd6ql98RXxfD47UDVwYOscoK5O+Uq2oOubb7at2qdpZx2jc6AKS0QWGGNGFrod2leVr2yLKWQMtoyxN+eeftp+BYN2TpMIbLedHVXac0/7NXSo/X2a7RZibSOlVPZS9Vc6AqU6VbZrGM2Zk3Bhu3SpzR2fNw9efdXmkDc2EmIUo3mBMH78VVFq/7U3jN3SRhcexQKV2JpMMbvt1nZNpq4esUm2oOuQITZ4yucoWHcOIkp5rpdSKrNsiimkDbb++1+491647z746CP7uy23hDPOgIMOgj32gPXWy2q7uraRUqVPAyhVPKJRnOq3cHrNJTTxWybPX4fA94/gMM8WfthlFzj3XBg1iuD8Awlf24NIRAhHfQS/3Q7He+wEtAYqseBJJHVKV1decKdKMctn2pkWUlDlSkR8wHzgC2PMYYVujyqMbIoptAu23n8a55H5MHWqDaAqKmD//eG3v7VrBG66qac2pAridG0jpUqfBlCqS7Ub9ViyxFbJe+45exX/7bd2dEleIGz8+Kt+x5SLvmBFr00IjPa1rlk0GPw3dCygiA9UfD5bBrsYSnenGvHK5yiYFlJQZewCYBHQObP0VUnIpphCS7DVHMYfFQLn/B0+XGsLP9x4Ixx7bNq1ATNuNyGIK8dKeUp1NzoHSuUkl/SvllGPRoPf10z95mfhLLrdPjhwIBx4IBx4IJPfPYKJ16xLJGJv/Pl8EI22HynJtQ2JJa3T/ZytUkmL0xEoVUidNQdKRAYDM4C/Ab/LNAKlfZUCYOFCQledR3DJXAJfVuHs/Qub7bDrrh3etM6BUqq0dXgOlKZFqJhURQ4g9QhO6MUwk87/lsa1A4jiIxyF4OqdcSZvYavkbbtty6TbQAj819vti9hRkmg085pFmYKXdAvIpns81+NSzEFJOVfiU93aFOBioE+hG6JKwPvvw6RJcN99OH374vzuz3DOOdC/f97eIlVFPK2Up1RpyyaFT9MiFNA2/auxEc4+2wY40HbxWYDQQ8uo+/OnTF+4M01sSJQKKiSKv9pH4IFzIUn/EX9xX1NjF2jPlKrnJXjpyAKz2R6XUkiL00IKqpyIyGHA18aYBSISSPO8ccA4gCFDhnRR61RRWboULr/crrzds6etGnTRRYR+/B/BRf/WUSGlVEaeAig3LeJQ3LSITm2RKnrxc4fABgwxLYHDgE8IXXAvo58YTwO7Y6gAhIoKwwEHVDBpUua1jGKPjxiReaTES/DS2QvM6vpC5a9UUjS7qT2BI0TkEKAH0FdE7jLGnBz/JGPMVGAq2BS+rm+mKpjGRrjuOvjrX21nccEFcOmlsOGGWhlPKZUVryNQGdMi9K5e9xE/QvTaa/DII62PVVQYAi9fCRP/RJAJhKUaY+wCtiJQXS1pg6dkF6iJaXbJLmC9BC+dvcCspsWVt1JK0eyOjDETgAkA7gjURYnBkyp9Oc8deu01OO00eO89+PnP4ZprYPjwloe1Mp5SKhsZAyivaRF6V697iQU1oRA89RQ0Nhp8EuWm6Dk49XVw3nkE9v8N/hN9nqvcZbpATfe41+AlU9paR9PaNC2ufJVaiqZS5SanUaK1a+FPf4Jrr4VBg2D2bBgzpt3TiqUynhaXUKo0eBmB8pQWobqfUAiCL0S4/ogXWPHoywQi9Tjn7gyXfgIbbYRDdiMyHZ2jpMGL6kyaolk6jDFBIFjgZqg8y3qU6OWX7Z27Dz6AM8+Eq6+GvsmncWdT9ryzaBqhUqUjYwClaREqmVAIAvtGCTeBjwA3bbcI56HbYYst2jwvm6Cms+coKdURmqKpVGF5HiVaswYuuwxuuAGGDoXnn7fpCxkUujKephEqVTp0Id0SUxST2H/8kbqz3ibctAdQQYQKzll0PiO+FZwtMr46pc6eo6RUR+kop1KF42mUaM4cOP10+PRTOP98uPJKWGedrm9sDooljVAplZkupFtCimIS++zZcM45nP35JdzCWYC0PHTQQbQpEFEUwZ5Squh01kK62dK+qut12hyf1avhkkvglltg883h9tth773zt/0U8r0/OgdKqeLS4YV0VeEVdBL7V1/ZBZnuuw+23pram/fg3+dJmxLmzz0Hc+fawA68BXsaZCmlVPfQaXN8nnkGzjgDvvgCLrwQrrgCevVK2YZ8BSidsT+FTiNUSnmjAVQJKcgcoGgUpk2Diy+Gn36yHdPFF+NUV3PG2/ZmX4wxrYEdZA72imJETSmlVJfI+xyf776zAdP06bD11rZoxKhRKZ+e74BH5ywp1X1VFLoByrvYHKC//CU/wUYoBJMn23+TWrQI9t0Xxo2DHXaAhQth4kSorgZsSfKePaHC/SsSsV81Na3Bns+XOthLNqKmlFKqPMXm+PjE1/E5Po89BttuC3V1cNllhB6/iclNcwgtSdWhJQ94OiKv+6OUKik6AlVivE5iz5Qal2z0B9zXOI04c6600dU669gRqNNOs9FRQltiRR1WrYJ//MMGQ+PH299nKvigVfWUUqr7yEup8KVL4YIL4KGHYPvt4fHHCW0YZr8Z+7WMLM0ZOyfptvNdpKEYSp8rpQpDA6gy5CU1LnH0p64OZsyAcKPBb6LUm2dxfnk8XHcdbLhhyveKBXSTJ9tsv2i0dTRpwoTMwd7YsfbfdAvsKqWUKg85z/FpbrZlyS+/3HZcV15p0/f8fuqeOJvGSCMAjZFG6t6uS/oeiQEPwOS5kzsU/OicJaW6Jw2gypCXYhOJoz/8tIbw2h5E8BGmiuCpM3Cmb+n5PbMdTUoM8mprs9pFpZRS3cXzz9tgaeFCOOQQuPFGGD48p03FAh5dtFYp1RE6B6oMeZl/1DKfalKE+l/PpPbBI/HTiE8i+Hv6CIzzHjy12Z7H+Vk6/0kppVRa77wDY8bAgQfaMuUPPghPPNEueKrdoRa/z48g+H1+anfIfEcu3/OhlFLdi45AlbBU85w8LThrDM6KJ3FmXgLvvQdjxlB/6gqCH2+Sc0nxbBYZ1flPSimlknrjDbj6anjgAejbF665Bs47r6WAEbQvRx4cG8xqLpIuWquU6ggNoEpUpnlOaYOZuXPh0kvhlVfsgoOPPQaHHYYjQlclMHgK8pRSSnUPxsALL8BVV9mUvT594KKL7BIaNTVtnpoq/S6bFDwtAKGU6ggNoEpU1ovqRqN2scFrr4X6ekL9Dyd41KsEfrczzt6F+TPIZsRKKaVUGWpogEcesaNMCxbAwIE2iDrrLFh33aQvydf6S1oAQimVKw2gSpTnFLiGBrjrLltNb9EiGDSI0Ll3Mfr2XxB+XPA/owvYKqWU6kLG2GBp+nSYOdMuiLv55jB1KpxyCvTokfblmn6nlCo0DaCKRLL5TOnWcsqYAvfZZ7Zzuvlm+OYb2HFHuPNOOP54gtf6sxu9UkoppTp1gElaAAAWPklEQVTqq6/g7rtt3/TuuzZQOvpoOPVUm5Pu82XcRGzu05SDp7DipxWafqeUKggNoIpAqkVtM63l1C4FbtUqQle9SPC+5QQ+m47DPDj0UFv+NRBoWQhXCzgopZTKp8SiDi3CYVs57447YPZse+du1Ci45RY44QTo1y+r99DS40qpYqABVBGoq7OZdsa0LemdbJSo3ahUOAxPPw133kno0a8Z3fQUYfz4K0+j/t5vcI4Z1O79tICDUkqpfEka2DRuYNdruusuWLECNtrIFoUYOxa23jrpNjIVdMjX3CellOooDaAKLBSC22+3wRNAZWXriFDiKFEoBPvtB+GwwV9pmHP4dTgvXmU7pw02IDhyGuFXexKJCmEDwQ8GtauqFx+ATZjQZbuplFKqTLUJbJobqfu/kwgu/JzAEh/O/j+H006zazlVJr/k8DqypHOflFLFQgOoAgsG7SgT2Ay7005rHRFKHCU6+7QGGhurAaGxCeoe7Ytz7AF20u1BBxGYX4V/dOrUvEylz5VSqlSJyCZAHTAQiAJTjTH/LGyruofAxntQaYSogYpIlOnrfU7z/oK/sor6sb/NOErkdWRJS48rpYqFBlAFljgfqTZuAfWWOU4LFsCv/gV3jgLOaH1C7Vi4fVyb56dLzcu69LlSSpWOZuBCY8wbItIHWCAizxlj3it0w8pWJGKLE914KWZMM1RAtFKIIESJEo42eUqzy2ZkSUuPK6WKQcYASu/qda5kQU8oBME5UQLROThP/hHmzYNevag9bCtuf8rQ1CRUVQm1Z1Qn3V6qoEiLRyilypUxZhmwzP3+BxFZBGwMaADVGebPh7PPhvnzCZ6wMZHKCgxRDIKvwocY8ZxmpyNLSqlS42UESu/qdbL4oGfqVMN55xgiEUM1DvWDN8GZciKMHYvTrx/BNKXNvbyPFo9QSpU7ERkG7AS8WtiWlKHvvoM//MFW0RswAO6+m8Bew/DfeUDLCFIuJcZ1ZEkpVUoyBlDd8a5euvWXOvV9Z37GuWdtTLOpBCpolB4Ez7oX54KKluekG2HyoqOvV0qpYiYi6wCzgPHGmNVJHh8HjAMYMmRIF7euxD37rK2i9/XX8JvfwJ//DOuuiwM6gqSU6laymgOV7q5euXRKXV1oIRSC4LNhAv+7heD9y4maPwMCGHyVFQT277z3VkqpciIiVdjg6W5jzEPJnmOMmQpMBRg5cqTpwuaVroYGW7Z1yhTYdlu7ntNOO7V5io4gKaW6E88BVKa7euXSKXVloYVQCEbvFyHcWIGfXzNlrwepnu+jMQwVFcKNN+pokVJKeSEiAkwDFhljrit0e8rGokVw4omwcCGcdx5cfTX07FnoVimlVEF5CqC83NUrF11WaKGxkeClLxBuPJAIlYQrKlhxSC31V+scJaWUysGewCnAOyLylvu7y4wxswvYppLTZkHb15fZlL2ePeHJJ+GQQwrdPKWUKgpeqvB1q7t6XVJo4cMP4bjjCLzdE79vNGEMfn9Fy/tp4KSUUtkxxryEzX9WOWqzoG1UqJ/WjLPt7vDggzB4cKGbp5RSRcPLCFS3u6uXzyAmVpCipgZWrIBA9AWc/zsKqqpwHruD+v5+HXFSSilVcG0WtI1C8LhdcSa/CNXtl8xQSqnuzEsVPr2rl6NYQYrGRohGDRUYqhlF/TbH4zx1OQwZgoMGTkoppQov0DQIf1OUsIDfV0Xgt//U4EkppZKoyPyU8hEKweTJ9t+ueH2sIEU0an+OUkEj1QRPugVKuFKhUkqpMmIMTJuGc9jZ1D9Zw19+djb1v3pRq+oppVQKWZUxL2aZ1m7qaHnyxNdPmeKm5KV4P3ALUvgiNEQMBh9giOKjZsOsd08ppZTqkDYFImLB0ccfw5ln2k5xv/1wZs7EGTCgsA1VSqkiVxYBlJfgKNvy5IkBWfzrGxttNddoNE0w9tVXODdeSH34Eyatcy3PrXEwRqiosIGXUkop1VXaFIjw+an/xTM4978CkybZjuyWW+CMM6CiWyWmKKVUTsoigPISHGVTnjxZQBb/ehH7XtFokveLRGxH9Ic/wNq1OJdfyqT9dmHuIdLmveMDtNg+JH7f0blRmUbllFJKdQ9tCkQ0hwlecgLOA8vg6KPhxhth0KBCN1EppUpGWQRQXoKjbMqTJwvIJkxofX1NDYwfn+T95s+Hs86CBQvggAPgX/+CLbfEoe17Q2uA5vPZgKy5ue33uaQZxutoyqJSSqnyERgWwO/zE25qwN8cIfBhGGbNgp//vNBNU0qpklMWAZTj2DlJs2bBMcekDhSSlSdPNkpTU2OzGIxJPmLkODBiRNzPW6+C8/4IN90EAwfCvffC8cfbaCjJe0+e3BqgxQpMGNP2ey9phulkm7KolFKqfDmLfqD+0XUJVq8lsOOROHPugH79Ct0spZQqSWURQIVCrSNCc+fa4MZLsJBslAbstiIRG0RNmWJ/l2w0xxll4J574OgL4Ztv4Pzz4YorYN11075v/IhZuhGodGmGmWSTsqiUUqoIRSKwdCmsXGknz/7wA6y3nq3iOniwPbmns3IlPP00PPAAPPIIzlZb4Uy9D/bZp2var5RSZaosAqisR1siEVi1iuCDhnBjDZGoEA4bgs83Q2VVS+lxEdtntdv+Ez/ifPwITJtmH9xtN5g9G3beOW0740exElP68j0HKpuURaWUUkVk+XL44x/hoYdsEJSMiJ23NHSo/aquth1UY6P99+uv4fXXbWfWvz9MnAiXXQY9enTtviilVBkqiwAq2WhL0gIKr71m8+fq6+GHHwgwCj/1hKnCH2kicPloWG99/NFZhKnEL1ECC26Apib8/JawVOKPNhG48kBgHmy4Idx8s61c5POlbeP/b+/+g6Su7zuOP993xyqi4ORQ4wiIGsfRhmk1iN0Y7SaHCQQmNMI0QlIdMyna1FakE0faMU2qHTLTmUAdbRqFa4/BBOxhLNMhofHMEjJZBQSpEpJqKchFBQvJWBJkdffdP757cD/2uO/d7u33s8vrMbMDe7cs788w8Oa1n1/lZruWLTv1/d4Bp1php9ySRRERCZQ7PPkk3HsvHDsGn/scfOxjUa9pbYVzz40C1euvw4EDpx7btsF770XNpecxYUIUmObMgeuvH7JHiYhIfHUdoE43o9MnrGz8Del/eyA61OHCC2HRIrjmGtIf+ABdh15izXOT4PhxuOJPSJOj6z8fIPs/U8j89vuks7sglaLrgm1k8x8lM2Uf6QVzYdajcO21sY981Z4kEREZ1MGD0SFEmzZFzWH1arj66qr+FmXvgRIRkWGr2wB1uhmd3oc05E8Uyd76COljj0WXNz38MIwf3+uNoOPB6PUdz19FV9cXo5kbAJaefFm69NpsFshA+iPDOyZce5JERM4ssQPL6tVw331R01qxItpPW6UZo54aWs9pZckPlpy6B+r2LoUoEZERqtsAdboZnUwGUmOcfKFAqpgnc9FeePb5aK/SMN6nt/6BbeXKvkeZD3VMuPYkiYicOQZcXDtYYHnoIfjqV+HjH4dVq+Dyywe8z0hnjXrXYGYUvUjRi+QLebL7swpQIiIjVLcB6nQzOulL32Dl+H9kQ/4PmP/ZIul17dBSfqiDvU//2aX+QWvDhuEvydOeJBGRM0Ofi2sHCywrVkTh6Y47olmofrNOsUNYjBqavInmpmYMI9WcovWcVpZvXa7lfCIiI1AXAarcUrlBZ3QOHSKXXsqSw+3km8aydZMxbfvp74bq/z7llgf2D1rz50dHpmtJnoiI9Hfy4tpS+MlMzfR9QXs7uW8uJXv3h8k88CXSZZbsxQphw6hh5ayVHPntES3nExGpUPABqlyY6R2i+gSjt9+Gtjayb84n3zS2dDz50LNDvd8nl4OvfS06CbZYPDW7tGzZwKDV5zJd9R4RESlJT07TdXsXa3avGfjNzk5yX/8SbXc2kW/ZS2rtJ8uGmCFDWMwa+i8BXL51eUXBTETkTBd8gIp9et2RI+Q++pdk93+W1r9YSOpbNuzZoZ6w1hOezKKVfz2/vn9g05I8ERE5nY7dHeQLeTp2d0Qh6eVfwaJFZP9oCvmW7tOGmMEC0HCkJ6erHsxERM50wQeoWKfXHTlCLr2Uttf+iRN2Nk2PNLF0KZx/fvnZocFOz+sJa8Vi9Nw9eoiISPjMbBbwD0AzsMrdv5FkPQOW4D3XTvrutTBtGpm//gapp+cNGWLKBaBKVSOYiYicyWIFqCSb0pCn1x09CrfcQnbfHE7Y2RS9ieL70d7cLVvKh6fBlgT2hLV33z0VnAoF3dkkIhI6M2sGHgNuAbqB7Wa20d1/llRNfWZ6rIXM362FK66AzZtJT5w4+BK/GhiNYCYicqYY8hbYXk1pNnANsNDMrhntwnpLp6M9SL33KS1fDrnN78DMmbBnD5m/n0NT86nh9ASf/sotCez9+3R1wV13wVlnRQci6YAIEZG6MAN4zd33uXseWAfMS7Kgnpmeh6Z+ka7vtJBmMjz7LEycePI1Hbs7eGLnE7StaSN3MJdgtSIiElecGaiTTQnAzHqaUiKf6p2cQTrhpHwMXc3nkt74DOnZv89j46K7cguFKACVCz5DLQns2dd0++06IEJEpI5cAhzs9bwbuCGhWk5Kd/2C9OJ/gQ99CLo2wwc/ePJ7lZ6yJyIiyYgToGI1JTNbDCwGmDJlSlWKKyebhfyJIoViE3layN7ZQXr2ZQAsXjz0yXhxL7TVAREiInXFynxtwC7WWvUqjh+H++6Db387+tSvszPamNtLucMcKrk4V0REaiNOgIrVlNz9ceBxgOnTp4/O0Qv5PJkdj5Aqfpk8KVJnN5O587I+L+kJPT1L8wYLUQpHIiINpRuY3Ov5JOCN/i+qSa86fBjmzoXt2+H+++Hhh2HMmAEv63+YA1DRxbkiIlIbcQJUrKY06o4ehVtvJb1lC10LzyJ79Z+Smdk0rEMiRESkYW0HrjSzy4BfArcBi2pexauvwqxZ8Oab8MwzMO/027B6H+ag+5lEROpDnACVfFM6dAhuugkOHIC1a0l//vMM1lJi3xslIiINw93fN7N7gM1EJ8a2u/uemhZRKEQHGx0/HjWfGTOG9ct1P5OISH0YMkAF0ZSeeCL6VG/LFnJjbia7fPD9S7HujRIRkYbj7puATYkVsHUrvP46PPXUsMMT6H4mEZF6EeseqMSb0rp1cNNN5MbcPOTyvLiHRIiIiFRVZyeMHQuf/vSI30L3M4mIhC9WgErUK6/Anj3w6KOxl+fpkAgREampYhE2bIjC07hxSVcjIiKjKPwAtX49NDXBggVk9ml5noiIBOinP4W33oIFC5KuRERERlnYAcod1q8nd92XybZfRCYz+PK8XE7L9kREJCGdndEN7nPmJF2JiIiMsrAD1K5d5F5tpW3MCvK7olmnlSsHvkxHl4uISGJ6lu/NmgXnnZd0NSIiMsqaki7gtNavJ9v0CfLFZgoFOHEC7rkHHnwwCky5XPSycnujREREamLbNnJ0s7wtRe5gLulqRERklIUboErL9zI3vEsqZTQ3R1uhCoWBQann6PLmZu2NEhGR2so9/Qhtd8CDv3qatjVtClEiIg0u3CV8L7wABw6Q/tvfpevKKCy1tsKSJQMPkdDR5SIikgh3snu/T/4jUPAC+UKe7P6sjiIXEWlg4QaodeuiDbnz5pGecCoUTZtWPijp6HIREam5HTvIvPRrUtenyFMg1ZwiMzWTdFUiIjKKggxQuZ8UyLZfSOaGJaQnTOjzPQUlEREJRmcn6bda6Jq/kezRnWSmZjT7JCLS4IILULkctM2E/In7ST0PXTkFJhERCZB7dHx5Wxvp3/kUaT6VdEUiIlIDwQWobBby7zVRwMgXnGxWAUpERAJUKMBXvgKXXpp0JSIiUkPBBahMBlJnWemgCNOJeiIiEqaWFrj77qSrEBGRGgsuQOlEPRERERERCVVwAQp0UISIiIiIiIQp3It0RUREREREAqMAJSIiIiIiEpO5e/Xf1Oxt4EAFbzER+N8qlRMCjSdsjTYeaLwxaTxhG+54LnX3C0armLjUqwZotPFA441J4wmbxhO2kYynbL8alQBVKTPb4e7Tk66jWjSesDXaeKDxxqTxhK3RxhNXo4270cYDjTcmjSdsGk/YqjkeLeETERERERGJSQFKREREREQkplAD1ONJF1BlGk/YGm080Hhj0njC1mjjiavRxt1o44HGG5PGEzaNJ2xVG0+Qe6BERERERERCFOoMlIiIiIiISHAUoERERERERGIKLkCZ2Swz+4WZvWZmDyRdTyXMrN3MDpvZK0nXUg1mNtnMfmRme81sj5ndm3RNlTCzs81sm5ntLo3n60nXVA1m1mxmu8zs35OupVJmtt/MXjazl8xsR9L1VMrMzjezTjP7eenvUTrpmkbKzK4q/bn0PN4xsyVJ11Ur6lXhUq+qD+pV4VKvivG+Ie2BMrNm4L+AW4BuYDuw0N1/lmhhI2RmNwPHgDXu/uGk66mUmV0MXOzuO83sPOBF4A/r+M/HgHHufszMxgA/Ae519+cTLq0iZrYUmA6Md/e5SddTCTPbD0x394a4yM/MOoCt7r7KzFLAOe7+66TrqlTp3+5fAje4eyUX09YF9aqwqVfVB/WqcKlXDS20GagZwGvuvs/d88A6YF7CNY2Yu/8YOJp0HdXi7m+6+87Sz/8P2AtckmxVI+eRY6WnY0qPcD5RGAEzmwTMAVYlXYv0ZWbjgZuB1QDunm+EhlTSBvz3mRCeStSrAqZeFT71qnCpV8UTWoC6BDjY63k3dfyPXiMzs6nAtcALyVZSmdISgpeAw8AP3b2uxwOsBO4HikkXUiUO/IeZvWhmi5MupkKXA28D/1xatrLKzMYlXVSV3AZ8N+kiaki9qk6oVwVLvSpc6lUxhBagrMzX6vpTlkZkZucCG4Al7v5O0vVUwt0L7v57wCRghpnV7fIVM5sLHHb3F5OupYpudPfrgNnAn5WWGtWrFuA64Fvufi3wG6Cu984AlJZ3fAb416RrqSH1qjqgXhUm9argqVfFEFqA6gYm93o+CXgjoVqkjNL66w3Ak+7+dNL1VEtpejoLzEq4lErcCHymtBZ7HfAJM1ubbEmVcfc3Sj8eBr5HtHSqXnUD3b0+Oe4kalL1bjaw090PJV1IDalXBU69KmjqVWFTr4ohtAC1HbjSzC4rJcXbgI0J1yQlpY2sq4G97v7NpOuplJldYGbnl34+FpgJ/DzZqkbO3Ze5+yR3n0r0d+c5d/9CwmWNmJmNK20Ap7R84JNA3Z4S5u5vAQfN7KrSl9qAutzU3s9Czqzle6BeFTT1qrCpV4VNvSqelmq9UTW4+/tmdg+wGWgG2t19T8JljZiZfRfIABPNrBv4G3dfnWxVFbkR+GPg5dJabIC/cvdNCdZUiYuBjtKpLE3AU+5e98epNpCLgO9F/xeiBfiOu/8g2ZIq9ufAk6X/dO8D7ky4noqY2TlEJ9HdlXQttaReFTz1Kqkl9arAjUavCuoYcxERERERkZCFtoRPREREREQkWApQIiIiIiIiMSlAiYiIiIiIxKQAJSIiIiIiEpMClIiIiIiISEwKUCIiIiIiIjEpQImIiIiIiMT0/y87oE96Z3qzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x720 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This dictionary defines the orders of polynomial model \n",
    "# to plot (keys) and corresponding subplots (values)\n",
    "models_to_plot = {1:421, 7:423, 14:425, 19:427}\n",
    "\n",
    "# Iterate through all powers and assimilate results\n",
    "for i in range(1,40):\n",
    "    coef_matrix_simple.iloc[i-1, 0:i+3] = linear_regression(data_in, data_out, power=i, models_to_plot=models_to_plot)\n",
    "coef_matrix_simple"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Illustrate the bias-variance tradeoff\n",
    "\n",
    "The in-sample MSE (blue) and out-of-sample MSE (green) are shown for each of the models (ordered by complexity). Note that only the first 30 models are shown. The in-sample MSE (bias) is observed to decrease with complexity. For less complex models, the in-sample MSE is paradoxically observed to be higher than the out-of-sample MSE, indicating that the model is underfitting. However, for the most complex models, the out-of-sample error is significantly larger than the in-sample error (variance)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = coef_matrix_simple['rss'][0:30].plot(color='blue', label='MSE on training sample')\n",
    "coef_matrix_simple['cross-rss'][0:30].plot(ax=ax, color='green',  label='MSE on validation sample')\n",
    "ax.legend( loc='upper left')\n",
    "ax.set_xlabel(\"Model complexity (Power of x)\")\n",
    "ax.set_ylabel(\"MSE\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
